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Romain Brette
romainbretteinsermfr
An ecological approach to neural computation
The view from the black box
Ɵ
Things in the world Sense data
decoding
coding
perceiverexternal world
THE BLACK BOX
Ɵ
Clark (2013) Whatever next Predictive brains situated agents and the future of cognitive science BBSAlso Kant (1781) Critique of pure reason
Poincareacutersquos answer
laquo To localize an object simply means to represent to oneself the movements that would be necessary to reach it It is not a question of representing the movements themselves in space but solely of representing to oneself the muscular sensations which accompany these movements and which do not presuppose the existence of space raquo
(Poincareacute 1905)
Sensorimotor contingencies
The villainous monster argument
laquo Vision is a mode of exploration of the world that is mediated by knowledge on the part of the perceiver of what we call sensorimotor contingencies raquo
OrsquoRegan amp Noeuml (2001) BBS
Invariant structure
Invariant structure ldquoPerceiving is a registering of certain definite dimensions of invariance in the stimulus flux together with definite parameters of disturbance The invariants are invariants of structure and the disturbances are disturbances of structure [hellip] The invariants specify the persistence of the environment and of oneselfrdquo
James Gibson (1979) The ecological approach to visual perception
Perception = identification of sensorysensorimotor laws (laquo pick-up of information raquo)
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
The view from the black box
Ɵ
Things in the world Sense data
decoding
coding
perceiverexternal world
THE BLACK BOX
Ɵ
Clark (2013) Whatever next Predictive brains situated agents and the future of cognitive science BBSAlso Kant (1781) Critique of pure reason
Poincareacutersquos answer
laquo To localize an object simply means to represent to oneself the movements that would be necessary to reach it It is not a question of representing the movements themselves in space but solely of representing to oneself the muscular sensations which accompany these movements and which do not presuppose the existence of space raquo
(Poincareacute 1905)
Sensorimotor contingencies
The villainous monster argument
laquo Vision is a mode of exploration of the world that is mediated by knowledge on the part of the perceiver of what we call sensorimotor contingencies raquo
OrsquoRegan amp Noeuml (2001) BBS
Invariant structure
Invariant structure ldquoPerceiving is a registering of certain definite dimensions of invariance in the stimulus flux together with definite parameters of disturbance The invariants are invariants of structure and the disturbances are disturbances of structure [hellip] The invariants specify the persistence of the environment and of oneselfrdquo
James Gibson (1979) The ecological approach to visual perception
Perception = identification of sensorysensorimotor laws (laquo pick-up of information raquo)
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Poincareacutersquos answer
laquo To localize an object simply means to represent to oneself the movements that would be necessary to reach it It is not a question of representing the movements themselves in space but solely of representing to oneself the muscular sensations which accompany these movements and which do not presuppose the existence of space raquo
(Poincareacute 1905)
Sensorimotor contingencies
The villainous monster argument
laquo Vision is a mode of exploration of the world that is mediated by knowledge on the part of the perceiver of what we call sensorimotor contingencies raquo
OrsquoRegan amp Noeuml (2001) BBS
Invariant structure
Invariant structure ldquoPerceiving is a registering of certain definite dimensions of invariance in the stimulus flux together with definite parameters of disturbance The invariants are invariants of structure and the disturbances are disturbances of structure [hellip] The invariants specify the persistence of the environment and of oneselfrdquo
James Gibson (1979) The ecological approach to visual perception
Perception = identification of sensorysensorimotor laws (laquo pick-up of information raquo)
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Sensorimotor contingencies
The villainous monster argument
laquo Vision is a mode of exploration of the world that is mediated by knowledge on the part of the perceiver of what we call sensorimotor contingencies raquo
OrsquoRegan amp Noeuml (2001) BBS
Invariant structure
Invariant structure ldquoPerceiving is a registering of certain definite dimensions of invariance in the stimulus flux together with definite parameters of disturbance The invariants are invariants of structure and the disturbances are disturbances of structure [hellip] The invariants specify the persistence of the environment and of oneselfrdquo
James Gibson (1979) The ecological approach to visual perception
Perception = identification of sensorysensorimotor laws (laquo pick-up of information raquo)
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Invariant structure
Invariant structure ldquoPerceiving is a registering of certain definite dimensions of invariance in the stimulus flux together with definite parameters of disturbance The invariants are invariants of structure and the disturbances are disturbances of structure [hellip] The invariants specify the persistence of the environment and of oneselfrdquo
James Gibson (1979) The ecological approach to visual perception
Perception = identification of sensorysensorimotor laws (laquo pick-up of information raquo)
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Models of natural systems
Robert Rosen (1985) Anticipatory Systems
Example a gas
observables pressure (P) volume (V) temperature (T)
linkage (relation) PV = constantT = Gibsonrsquos laquo invariant structure raquo
Also effect of actions on observables (=experiment)
= Gibsonrsquos laquo affordances raquo
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Subjective physicsHow do models of the world look like from the black box
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
Brette (2013) Subjective Physics Arxiv
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Subjective physicsHow do models of the world look like from the black box
Example binaural hearing
Subjective physics the laws that govern sensory and sensorimotor signals from the perspective of the perceiver
SL(t-d)=SR(t) for all t
head position
source location
SL(t)
SR(t)
Model
Brette (2013) Subjective Physics Arxiv
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
The Jeffress model of sound localization
Synchrony whenSR(t-δR)=SL(t-δL)
dR-dL = δL -δR
SR(t)=S(t-dR) SL(t)=S(t-dL)
S(t)
(invariant structure)
The neuron fires when a particular sensory model is satisfied(laquo hypothesis testing raquo)
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Biology of sound localizationFr
anke
n et
al
(201
5)
Ram
on y
Caj
al (1
907)
Loua
ge e
t al
(200
5)
laquo best delay raquo
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
ButThe best delay varies with tone frequency
SL(t-d)=SR(t) for all t
The neuron doesnrsquot signal this identity
N =186 cells
(max natural ITD = 350 micros)
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Back to acousticsFRFL = location-dependent acoustical filters(HRTFsHRIRs)
Sound propagation is more complex than pure delays
SL = FLSSR = FRS
ITD is frequency-dependent
Beacutenichoux V Reacutebillat M Brette R (2015) On the variation of ITD with frequency (In review)
Neurons tuned to a natural binaural invariant would have frequency-dependent best delay
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Sensory model for ecological acousticsIdealized acoustics (no head)
With a head
SR (t) = (FRS) (t)
Source S(t)
SL(t) = S(t-dL)
SR(t) = S(t-dR)
laquo Subjective raquo modelSL(t-δL) = SR(t-δR)
where δL + dL = δR + dRphysical model
Source S(t)
physical model
SL (t) = (FLS) (t) laquo Subjective raquo model(NL SL) (t)= (NR SR) (t)
where NL FL = NR FR
(example NL = FR and NR = FL)
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
ITD in cats
Reacutebillat M Benichoux V Otani M Keriven R Brette R (2014) Estimation of the low-frequency components of the head-related transfer functions of animals from photographs JASA 135 2534
Beacutenichoux V Fontaine B Karino S Joris PX Brette R (2015) Frequency-dependent time differences between the ears are matched in neural tuning (In review)
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Quantifying frequency-dependence in cellsbest delay (BD) best phase = BD f
BP = CDf + CP
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
Charateristic phase(Jeffress CP = 0)
Charateristic delay(Jeffress CD = BD)
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Quantifying frequency-dependence in acoustics
ITD IPD = ITD f
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
How is it possibleThe Jeffress model
δL
dL
δR
dR
Source S(t)
SL(t) = S(t-dL) SR(t) = S(t-dR)
Signals SL(t-δL) =SR(t-δR)
δL+dL = δR+dR
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
How is it possibleNew model
HRTFLHRTFR
Source S(t)
SL = HRTFL S SR = HRTFR S
Signals NL SL = NR SR
cochlear filter NL NR
Replace delays by filters(possibly including delays)
NL HRTFL
= NR HRTFR
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Cochlear disparity
NL
NR
Hypothesisdifference in frequency tuning between two sides+ axonal delays
Auditory nerve recordings
Pseudo-binaural tuning curve
cross-correlogram
CP = 023
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Does it work
FR
FL
γi
γi
GRj
GLj
Sounds noise musical instruments voice (VCV)
Acoustical filtering measured human HRTFs
Gammatone filterbank +more filters Spiking noisy IF
models
Coincidence detection noisy IF models
Goodman DF and R Brette (2010) Spike-timing-based computation in sound localization PLoS Comp Biol 6(11) e1000993 doi101371journalpcbi1000993
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Does it workDurkovic (2011) Low latency localization of multiple sound sources in reverberant environments JASA Express Letters
Replace coincidence detection with cross-correlation
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Generalizationcomputing with synchrony
A
B
laquo Synchrony receptive field raquo = set of stimuli S making A and B fire synchronously
= S | NA(S) = NB(S)
a law followed by sensory signals Sor laquo invariant structure raquoor sensory model
no response
coincidence detection
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Uzz
el amp
Chi
chiln
isky
(200
4)
Spike timing precision in primate retina
Time (s)
Jitter (ms)
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Binocular disparityHypothesis two ganglion cells synchronize when there is an object at particular depth
Conduction velocity in the optic tract compensate for conduction time differences in the retina
Stanford (1987) Science
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Binocular disparityPsychophysics
random dynamic Gabors Introduce interocular delay
Sensitivity to disparity
0 8 25 delay (ms)
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Visual edges
Synchrony receptive field (AB) =translation-invariant image
In LGN correlation is tuned to orientation
Stanley et al (2012) Visual Orientation and Directional Selectivity through Thalamic Synchrony J Neurosci
LGN
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Olfaction
fluctuates (air turbulences)
C1(t) = a1 x [O](t)
C2(t) = a2 x [O](t)
C3(t) = a3 x [O](t)
C4(t) = a4 x [O](t)
A
B
C
receptor neurons with different sensitivities
sA x a1 x [O](t)
sB x a1 x [O](t)
sC x a4 x [O](t)
s x aA and C synchronize for some odor (sA x a1 = sC x a4)
B and C synchronize for another odor (sB x a1 = sC x a4)
odor affinities
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Olfaction
odorconcentration
Brette R (2012) Computing with neural synchrony PLoS Comp Biol
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
PitchPitch asymp perceptual correlate of the period of a sound
But many aspects of pitch depend on harmonic content (resolvability)
Hypothesis pitch is the perceptual correlate of the regularity structure of the basilar membrane vibration
S(xt) = S(yt-d)
Sensory model
Laudanski et al (2014) A structural theory of pitch eNeuro
Prediction level-dependence of pitch for low frequency tones
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
PitchPsychophysics
The pitch of low-frequency pure tones depends on sound level
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
Thank youSensory models from the black box
Brette (2013) Subjective Physics arXiv
Computing with synchrony Brette (2012) Computing with neural synchrony PLoS Comp Biol Rossant Leijon Magnusson Brette (2011) Sensitivity of noisy neurons to coincident inputs J Neurosci
Sound localization Goodman amp Brette (2010) Spike-timing-based computation in sound localization PLoS Comp
Biol Reacutebillat Beacutenichoux Otani Keriven Brette (2014) Estimation of the low-frequency
components of the head-related transfer functions of animals from photographs JASA Beacutenichoux Reacutebillat Brette (2015) On the variation of interaural time di1113088fferences with frequency JASA (revision) Beacutenichoux Fontaine Karino Joris Brette (2015) Frequency-dependent time differences between the ears are matched in neural tuning eLife (revision)
Pitch Laudanski Zheng Brette (2014) A structural theory of pitch eNeuro
Simulation Goodman amp Brette (2009) The Brian simulator Front Neurosci
