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Convergence and stability in networks with spiking neurons. Stan Gielen Dept. of Biophysics Magteld Zeitler Daniele Marinazzo. Overview. What’s the fun about synchronization ? Neuron models Phase resetting by external input Synchronization of two neural oscillators - PowerPoint PPT Presentation
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Convergence and stability in networks with spiking neurons
Stan GielenDept. of Biophysics
Magteld ZeitlerDaniele Marinazzo
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
The neural code
Firing rate
Recruitment
Synchronous firingNeuronalassembly
Neuronal assemblies are flexible
Why flexible synchronization ?
Stimulus driven; bottom-up processFrom Fries et al. Nat Rev Neurosci.
Synchronization of firing related to attention
Riehle et al. Science, 1999
Evidence for Top-Down processes on coherent firing
Coherence between sensori-motor cortex MEG and muscle EMG
Schoffelen, Oosterveld & Fries, Science in press
Before and after a visual warning signal for the “go” signal to start a movement
Functional role of synchronization
Schoffelen, Oosterveld & Fries, Science in press
Questions regarding initiation/disappearence of temporal
coding
• Bottom-up and/or top-down mechanisms for initiation of neuronal synchronization ?
• Stability of oscillations of neuronal activity• functional role of synchronized neuronal
oscillations
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
Leaky-Integrate and Fire neuron)()()( tRItV
dttdV
m
For constant input I mm tt eRIVeRItV /0/ )1()(
Small input Large input
Conductance-based Leaky-Integrate and Fire neuron
)()()( tRItVdttdV
m
Membrane conductance is a function of total input, and so is the time-constant.
With increasing synaptic input, the neuron changes from an integrator to a co-incidence detector.
Synaptic processes
Conductance-based Leaky-Integrate and Fire neuron
)()()( tRItVdttdV
m
Membrane conductance is a function of total input, and so is the time-constant.
With increasing synaptic input, the neuron changes from an integrator to a co-incidence detector.
mmmmm GCCR /
Conductance-based Leaky-Integrate and Fire neuron
With increasing synaptic input, the neuron changes from an integrator to a co-incidence detector.
τ = 40 ms τ = 2 ms
)()()( tRItVdttdV
m
Hodgkin-Huxley neuron
V mV
0 mV
V mV
0 mV
ICINa
Membrane voltage equation
-Cm dV/dt = gmax, Nam3h(V-Vna) + gmax, K n4 (V-VK ) + g leak(V-Vleak)
K
V (mV)
mmOpen Closedm
mmProbability:
State:
(1-m)
Channel Open Probability:
dt
dm m)1( m m m
hhdtdh
hh )1(
Gating kinetics
m.m.m.h=m3h
mm
mm
mm
1
Actionpotential
Simplification of Hodgkin-HuxleyFast variables• membrane potential V• activation rate for Na+
m
Slow variables• activation rate for K+ n• inactivation rate for
Na+ h-C dV/dt = gNam3h(V-Ena)+gKn4(V-EK)+gL(V-EL) + I
dm/dt = αm(1-m)-βmm
dh/dt = αh(1-h)-βhh
dn/dt = αn(1-n)-βnn
Morris-Lecar model
Phase diagram for the Morris-Lecar model
Phase diagram for the Morris-Lecar model
Linearisation around singular point :
WV
bV
WV
dtd
1)1( 2
*
*
WWW
VVV
Phase diagram
Phase diagram of the Morris-Lecar model
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
Neuronal synchronization due to external inputT
ΔTΔ(θ)= ΔT/T
Synaptic input
Neuronal synchronizationT
ΔTΔ(θ)= ΔT/T
Phase shift as a function of the relative phase of the external input.
Phase advance
Hyperpolarizing stimulus
Depolarizing stimulus
Neuronal synchronizationT
ΔTΔ(θ)= ΔT/T
Suppose:
• T = 95 ms
• external trigger: every 76 ms
• Synchronization when ΔT/T=(95-76)/95=0.2
• external trigger at time 0.7x95 ms = 66.5 ms
ExampleT=95 ms
P=76 ms = T(95 ms) - Δ(θ)
For strong excitatory coupling, 1:1 synchronization is not unusual. For weaker coupling we may find other rhythms, like 1:2, 2:3, etc.
Neuronal synchronizationT
ΔTΔ(θ)= ΔT/T
Suppose:
• T = 95 ms
• external trigger: every 76 ms
• Synchronization when ΔT/T=(95-76)/95=0.2
• external trigger at time 0.7x95 ms = 66.5 ms
StableUnstable
Convergence to a fixed-point Θ* requires
Substitution of and expansion near gives
Convergence requires
and constraint gives
TPnnn /)(1
TPnnn /)(1
TP /)( * |||| **
1 nn
n= n* *
nn 1 nn TP
)(/)()( *
1)(1
n
n n
n
1 <1
-1< 1)(<1 and so –2 < )(<0
T
P
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
Excitatory/inhibitory interactions
excitation-excitation
inhibition-inhibition
excitation-inhibition
Behavior depends on synaptic strength ε and size of delay Δt
Excitatory interactions
excitation-excitation
Mirollo and Strogatz (1990) proved in a rigorous way that excitatory coupling without delays always leads to in-phase synchronization.
Stability for two excitatory neurons with delayed coupling
Return map)(1 kk R
For two neurons with excitatory coupling strength = 0.1 and time delay = 0.2
Ernst et al. PRL 74, 1995
)()()(: kBkAkBk ttt
if tk is time when oscillator A fires
Summary for excitatory coupling between two neurons
• In-phase behavior for excitatory coupling without time delays
• tight coupling with a phase-delay for time delays with excitatory coupling.
Inhibitory interactions
excitation-excitation
Inhibitory couplingfor two identical leaky-integrate-and-fire neurons
Out-of-phase stable In-phase stable
Lewis&Rinzel, J. Comp. Neurosci, 2003
Phase-shift function for neuronal synchronizationT
ΔTΔ(θ)= ΔT/T
Phase shift as a function of the relative phase of the external input.
Phase advance
Hyperpolarizing stimulus
Depolarizing stimulus
Phase-shift functionfor inhibitory coupling
0)( *
d
dG
for stable attractor
Increasing constant input to the LIF-neurons
I=1.2
I=1.4
I=1.6
Bifurcation diagram for two identical LIF-neurons with inhibitory coupling
Bifurcation diagram for two identical LIF-neurons with inhibitory coupling
Time constant for inhibitory synaps
Summary for inhibitory coupling
Stable pattern corresponds to• out-of-phase synchrony when the time
constant of the inhibitory post synaptic potential is short relative to spike interval
• in-phase when the time constant of the inhibitory post synaptic potential is long relative to spike interval
Inhibitory coupling with time delays
Stability for two inhibitory neurons with delayed coupling
Return map)(1 kk R
For two neurons with excitatory coupling strength = 0.1 and time delay = 0.2
Ernst et al. PRL 74, 1995
Stability for two excitatory neurons with delayed coupling
Return map)(1 kk R
For two neurons with excitatory coupling strength = 0.1 and time delay = 0.2
Ernst et al. PRL 74, 1995
)()()(: kBkAkBk ttt
if tk is time when oscillator A fires
Summary about two-neuron coupling with delays
• Excitation leads to out-of-phase behavior• Inhibition leads to in-phase behavior
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
A network of oscillators with excitatory coupling
Winfree model of coupled oscillators
0
P(Θj) is effect of j-th oscillator on oscillator I (e.g. P(Θj) =1+cos(Θj)
R(Θi) is sensitivity function corresponding to contribution of oscillator to mean field.
N-oscillators with natural frequency ωi
Ariaratnam & Strogatz, PRL 86, 2001
Averaged frequency ρi as a function of ωi
65.0locking partial locking
incoherence partial deathslowest oscillators stop
Frequency range of oscillators Ariaratnam & Strogatz, PRL 86, 2001
Phase diagram assuming uniform distribution of natural frequencies
SummaryA network of spontaneous oscillators with different natural
frequencies can give – locking– partial locking– incoherence– partial death
depending on strength of excitatory coupling and on distribution of natural frequencies.
Excitatory coupling can cause synchrony and chaos !
Role of excitation and inhibition in neuronal synchronizationin networks with excitatory and inhibitory neurons
Borgers & Kopell
Neural Computation 15, 2003
Role of excitation and inhibition in neuronal synchronizationin networks with excitatory and inhibitory neurons
gEE
gIE
gII
gEI
Mutual synchronizationAll-to-all connectivity Sparse connectivity
IE=0.1; II=0; gEI=gIE=0.25; gEE=gII=0; τE=2 ms; τI=10 ms
gEE
gIE
gII
gEI
Main messageSynchronous rhythmic firing results from• E-cells driving the I-cells• I-cells synchronizing the E-cellsSynchronization is obtained for• Continuous drive to E-neurons• Relatively strong EI connections• Short time decay of inhibitory post synaptic
potentials
Simple (Theta) neuron modelNeuronal state represented as phase on the unit-circle
with input I (in radians) and membrane time-constant τ.
When I<0: two fixed points :
unstable
stable
I<0 I=0 I>0
Saddle-node bifurcation
: spiking neuron
unstable
stable
I<0 I=0 I>0
Spiking neuron
Saddle-node bifurcation
Time interval between spikes
-π
π
-π
π
=
Sufficient conditions for synchronized firing
E=>I synapses too weak External input to I-cells
I=>E synapses too weakRhythm restored by adding I=>I synapses
Conditions for synchrony
•E-cells receive external input above threshold
• I-cells spike only in response to E-cells; Relatively strong EI connections
• I=>E synapses are short and strong such that I-cells synchronize E-cells
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
Possible role of feedback
Data from electric fish
Correlated input
Uncorrelated input
Doiron et al. Science, 2004
Possible role of feedback
feedbacknoise
Doiron et al. Science, 2003Doiron et al. PRL, 93, 2004
Feedback to retrieve correlated input
data model
Experimental data
cAxxxx jjjj 22*,0,0
* )()()()(
Linear response analysis gives
Power in range
2-22 Hz
40-60 Hz
What happens to our analytical formula when FB comes from a LIF neuron?
2
22222*
,0,0*
|1|||)(2||)()()()(
gKAgKAgKAAcAxxxx jjjj
0 50 100 150
15
20
25
30
freq (Hz)
S (s
pike
s2 /s)
FB from a linear unit
FB from LIF neuron G
D = 18 msec
g = - 1.2G = 6 msec, but we obtain the same line with G = 18 msec
τDelay= 18 ms
Feedback gain = -1.2τLIF = 6 ms (longer time constant, same result)
)()( * jj xx
Paradox with between results by Kopell (2004) and Doiron (2003, 2004) ?
• Börgers and Kopell (2003): spontaneous synchronized periodic firing in networks with excitatory and inhibitory neurons
• Doiron et al: Feedback serves to detect common input: no common input no synchronized firing.
0 20 40 60 80 100 120
0
100
200
300
400
500
600
700
800Spectrum with FB from LIF neuron and C1=0
Short time constant for inhibitory neuron: τLIF = 2 ms
LIFSynchronized firing even without correlated input
C=0
0 20 40 60 80 100 1200
50
100
150
200
250Spectrum with FB from LIF neuron and C1 =1
0 20 40 60 80 100 1200
100
200
300
400
500
600
700
800Spectrum with FB from LIF neuron and C1=0
LIF
Uncorrelated input Fully correlated input
For small time constant of LIF-neuron, network starts spontaneous oscillations of synchronized firing
Sufficient conditions for synchronized firing in the Kopell model
E=>I synapses too weak External input to I-cells
I=>E synapses too weakRhythm restored by adding I=>I synapses
Conditions for synchrony
•E-cells receive external input above threshold
• I-cells spike only in response to E-cells; Relatively strong EI connections
• I=>E synapses are short and strong such that I-cells synchronize E-cells
10 20 30 40 50 60 70 80 90 100 110 1200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1FB to all
CI 60 Hz to P1CI 45 Hz to P1CI 90 Hz to P1
10 20 30 40 50 60 70 80 90 100 110 1200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9no FB
CI 60 Hz to P1CI 45 Hz to P1CI 90 Hz to P1
No feedback; input at 50, 60 or 90 Hz With feedback; input at 50, 60 or 90 Hz
LIF LIFLIF with small τ
Time constant of inhibitory neuron is crucial !
Short time constant: neuron is co-incidence detectorBörgers and Kopell (2003): spontaneous synchronized
periodic firing in networks with excitatory and inhibitory neurons
Long time constant:Doiron et al: Feedback serves to detect common input:
no common input no synchronized firing.
Overview• What’s the fun about synchronization ?• Neuron models• Phase resetting by external input• Synchronization of two neural oscillators• What happens when multiple oscillators are coupled ?• Feedback between clusters of neurons• Stable propagation of synchronized spiking in neural
networks• Current problems
Propagation of synchronous activity
Diesman et al., Nature, 1999
•Leaky integrate-and-fire neuron:
•20,000 synpases; 88% excitatory and 12 % inhibitory•Poisson-like output statistics
‘Synfire chain’
i
iIVdtdV
Propagation of synchronous activity
Activity in 10 groups of 100 neurons each.
Under what conditions
•preservation
•extinction
of synchronous firing ?
Critical parameters• Number of pulses in volley (‘activity’)• temporal dispersion σ• background activity• integration time constant for neuron ( τ = 10 ms)
•activity a
•dispersion σ
Spike probability versus input spike number as function of σ
Temporal accuracy versus σin for various input spike numbers
Temporal accuracy versus σin for various input spike numbers
Output less precise
Output more precise
State-space analysis
Model parameters:
# 100 neurons
Stable attractor
Transmission function for pulse-packet for group of 100 neurons.
Evolution of synchronous spike volley
State-space analysis
Model parameters:
# 100 neurons
Attractor
Saddle point
Dependence on size of neuron groups
• a minimum of 90 neurons are necessary to preserve synchrony
•fixed point depends on a, σ and w.
N=80 N=90 N=100 N=110
a-isocline
σ-isocline
Summary
• Stable modes of coincidence firing.• Attractor states depend on number of
neurons involved, firing rate, dispersion and time constant of neurons.
Further questions
• What happens for correlated input from multiple groups of neurons ?
• What is the effect of (un)correlated excitation and inhibition ?
• What is the effect of lateral interactions ?• What is the effect of feedback ?
Summary• Excitatory coupling leads to chaos; inhibition lead to
synchronized firing.• Synchronization can easily be obtained by networks of
coupled excitatory and inhibitory neurons. In that case the frequency of oscillations depends on the neuronal dynamics and delays, not on input characteristics
• There is no good model yet, which explains the role of input driven (bottom-up) versus top-down processes in the initiation of synchronized oscillatory activity.
• The role and relative contribution of feedforward (stochastic resonance) and feedback in neuronal synchronization is yet unknown.