Fireflies on the Water How much stochastic is neuronal activity · Synaptic activity is intense and...

Unité de Neurosciences, Information et Complexité (UNIC)

CNRSGif-sur-Yvette, France

http://cns.iaf.cnrs-gif.fr

How much stochastic is neuronal activity ?

Alain Destexhe

FACETS(EU IST)

Yayoyi Kusama, Fireflies on the Water

Contributors:Theory: Claude Bedard, Sami El Boustani, Olivier Marre,

Serafim Rodrigues, Michelle Rudolph (UNIC),Experiments: Diego Contreras (U Penn, USA), Igor Timofeev,

Mircea Steriade (Laval University, Canada)

WessbergCrist & Nicolelis

(2002)

Ensemble activityin the cortex of abehaving rhesus

monkey

Neuronal activity in awake monkeyComplex spatiotemporal patterns of neuronal discharges

Plan

1. Characterization of neuronal activity in theneocortex of awake animals

2. Characterization of LFPs

3. Modeling neuronal activity in awake cortex

Multisite bipolar LFP recordings

Destexhe et al., J. Neurosci.,1999

Awake

Multisite bipolar LFP recordings

Destexhe et al., J. Neurosci.,1999

VLC media file(.mp3)

VLC media file(.mp3)Data: Destexhe, Contreras & Steriade, J. Neurosci. 1999

Music: http://www.archive.org/details/NeuronalTones

Multiunit extracellular recordings in awake cats

Wake: Poisson:

Multiunit extracellular recordings in awake cats

Softky & Koch, J Neurosci. 1993Bedard, Kroger & Destexhe, Phys Rev Lett 2006

Apparent stochastic dynamics!

Multiunit extracellular recordings in awake cats

Bedard, Kroger & Destexhe, Phys Rev Lett 2006

Apparent stochastic dynamics!

Multiunit extracellular recordings in awake cats

Marre, El Boustrani, Fregnac & Destexhe(Phys Rev Lett, 2009)

Correlated

Statistics of spike patterns in cat parietal cortex

Uncorrelated

Intracellular recordings in awake and sleeping animals

(Courtesy of Igor Timofeev, Laval University, Canada)

Synaptic “noise” in vivo

Pare et al.J Neurophysiol. 1998

Steriade et al.J Neurophysiol. 2001

Destexhe et al.Nature ReviewsNeurosci. 2003

Conductance measurements in vivo

Paré et al., J. Neurophysiol. 1998Destexhe et al., Nature Reviews Neurosci. 2003

Characterization of up-states in vivo

Microperfusion of TTX in cat parietal cortexunder ketamine-xylazine anesthesia

Paré et al., J. Neurophysiol. 1998Destexhe et al., Nature Reviews Neurosci. 2003

Characterization of up-states in vivo

Vm distributionsin different network states

Destexhe & RudolphNeuronal Noise

Rudolph et al.J. Neurophysiol 2005

J. Neurosci. 2007

Characterization of up-states in vivo

Destexhe & RudolphNeuronal Noise

Rudolph et al.J. Neurophysiol 2005

J. Neurosci. 2007

Conductance measurements in different network states

Rudolph, Pospischil, Timofeev &Destexhe, J. Neurosci, 2007

Conductance measurementsin awake cats

Extracting conductances from in vivo activity

Spike-triggered averages of conductances

Rudolph et al.,J. Neurosci,2007

Characterization of up-states in vitro

Destexhe & RudolphNeuronal Noise

(data fromHasenstaub & McCormick)

Characterization of up-states in vitro

Destexhe & RudolphNeuronal Noise

(data fromHasenstaub & McCormick)

Characterization of up-states in vitro

Destexhe & RudolphNeuronal Noise

(data fromHasenstaub & McCormick)

Synaptic activity is intense and noisy,essentially Gaussian distributed (bothfor Vm and conductances)

Responsible for a “high-conductance state”(3 to 5-fold larger than resting conductance)

Statistics of neuronal activity is very closeto Poisson processes

Importance of inhibition (both for absoluteconductance and for the dynamics ofspike initiation)

Characterizing neuronal activity

Destexhe & Rudolph, Neuronal Noise, Springer 2010

Conclusions

Plan

1. Characterization of neuronal activity in theneocortex of awake animals

2. Characterization of LFPs

3. Modeling neuronal activity in awake cortex

PSD of Local Field Potentials

Bedard et al.,Phys Rev Lett 2006

Modeling LFPs

“Diffusive” LFP Model

Bedard & Destexhe, Biophysical Journal, 2009

Coulomb’s law:

Ionic diffusionin homogeneousmedium

Electrode

PSD of the LFP:

Modeling LFPs

Bedard & Destexhe, Biophysical Journal, 2009

Transfer function LFP - Vm activity

Bedard, Rodrigues,Roy, Contreras &DestexheSubmitted

Fitting differenttransferfunctions toexperimentaldata alsosuggestsWarburgimpedance

“Avalanche dynamics” from LFPs in vivo

Petermann et al., PNAS 2009

Avalanche analysis from LFP activity (awake cat)

Touboul & Destexhe,PLoS One, 2010

Avalanche analysis from LFP activity (awake cat)

Avalanche analysis from LFP activity (awake cat)

Avalanche analysis from LFP activity (awake cat)

Shuffled LFP peaks (random process!)

Touboul & Destexhe,PLoS One, 2010

Avalanche analysis from LFP activity (awake cat)

Shuffled LFP peaks (random process!)

Touboul & Destexhe,PLoS One, 2010

LFPs are broad-band with 1/f scaling at low freq.

1/f scaling can be explained by effect of diffusion

Power-law distributions from LFP peaks can alsobe explained by thresholding procedure

Similar to neuronal activity, a lot can be explainedby purely stochastic mechanisms...

Characterizing LFP activity

Conclusions

Plan

1. Characterization of neuronal activity in theneocortex of awake animals

2. Characterization of LFPs

3. Modeling neuronal activity in awake cortex

Network models of self-sustained irregular states

Network models of asynchronous irregular states

Brunel, J Physiol Paris, 2000

Self-sustained asynchronous irregular states

Vogels & Abbott,J Neurosci 2005

El Boustani & Destexhe,Neural Computation 2009

Analysis of AI states

El Boustani et al.,J Physiol Paris, 2007

Analysis of AI states

El Boustani et al.,J Physiol Paris, 2007

Analysis of AI states

El Boustani et al.,J Physiol Paris, 2007

Analysis of AI states

El Boustani et al.,J Physiol Paris, 2007

20 timestoo many!

Modulation of information transfer by network activity

How to obtain models consistentwith conductance measurements ?

El Boustani & Destexhe,Neural Computation 2009

Mean-field model of AI statesMacroscopic modeling of AI states in spiking networks

Optical imaging 1 pixel = network ofrandomly-connected neurons

Mean-field model of AI states

Mean-field model of AI states

Model predictionNumerical simulation Difference

Conductancemaps

Mean-field model of AI states

Network models with realistic conductance patternsBest model: N=16000, 320 synapses/neuron

Vogels & Abbott, J Neurosci, 2005

Comparison

Network models with realistic conductance patterns

Conclusions

Randomly connected networks of IF neuronscan generate dynamics which reproduceexperimental observations in the awake brain...

... except for conductances measurements!

Mean-field models can be used to identifynetwork configurations with correctconductance state (work in progress...)

Modeling the awake neocortex

Thanks to the team...Michelle Rudolph

MartinPospischilSami

El BoustaniClaudeBedard

OlivierMarre

JonathanTouboul

SerafimRodrigues