fireflies on the water how much stochastic is neuronal activity · synaptic activity is intense and...
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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)
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WessbergCrist & Nicolelis
(2002)
Ensemble activityin the cortex of abehaving rhesus
monkey
Neuronal activity in awake monkeyComplex spatiotemporal patterns of neuronal discharges
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Plan
1. Characterization of neuronal activity in theneocortex of awake animals
2. Characterization of LFPs
3. Modeling neuronal activity in awake cortex
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Multisite bipolar LFP recordings
Destexhe et al., J. Neurosci.,1999
Awake
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Multisite bipolar LFP recordings
Destexhe et al., J. Neurosci.,1999
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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:
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Multiunit extracellular recordings in awake cats
Softky & Koch, J Neurosci. 1993Bedard, Kroger & Destexhe, Phys Rev Lett 2006
Apparent stochastic dynamics!
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Multiunit extracellular recordings in awake cats
Bedard, Kroger & Destexhe, Phys Rev Lett 2006
Apparent stochastic dynamics!
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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
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Intracellular recordings in awake and sleeping animals
(Courtesy of Igor Timofeev, Laval University, Canada)
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Synaptic “noise” in vivo
Pare et al.J Neurophysiol. 1998
Steriade et al.J Neurophysiol. 2001
Destexhe et al.Nature ReviewsNeurosci. 2003
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Conductance measurements in vivo
Paré et al., J. Neurophysiol. 1998Destexhe et al., Nature Reviews Neurosci. 2003
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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
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Characterization of up-states in vivo
Vm distributionsin different network states
Destexhe & RudolphNeuronal Noise
Rudolph et al.J. Neurophysiol 2005
J. Neurosci. 2007
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Characterization of up-states in vivo
Destexhe & RudolphNeuronal Noise
Rudolph et al.J. Neurophysiol 2005
J. Neurosci. 2007
Conductance measurements in different network states
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Rudolph, Pospischil, Timofeev &Destexhe, J. Neurosci, 2007
Conductance measurementsin awake cats
Extracting conductances from in vivo activity
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Spike-triggered averages of conductances
Rudolph et al.,J. Neurosci,2007
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Characterization of up-states in vitro
Destexhe & RudolphNeuronal Noise
(data fromHasenstaub & McCormick)
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Characterization of up-states in vitro
Destexhe & RudolphNeuronal Noise
(data fromHasenstaub & McCormick)
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Characterization of up-states in vitro
Destexhe & RudolphNeuronal Noise
(data fromHasenstaub & McCormick)
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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
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Plan
1. Characterization of neuronal activity in theneocortex of awake animals
2. Characterization of LFPs
3. Modeling neuronal activity in awake cortex
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PSD of Local Field Potentials
Bedard et al.,Phys Rev Lett 2006
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Modeling LFPs
“Diffusive” LFP Model
Bedard & Destexhe, Biophysical Journal, 2009
Coulomb’s law:
Ionic diffusionin homogeneousmedium
Electrode
PSD of the LFP:
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Modeling LFPs
Bedard & Destexhe, Biophysical Journal, 2009
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Transfer function LFP - Vm activity
Bedard, Rodrigues,Roy, Contreras &DestexheSubmitted
Fitting differenttransferfunctions toexperimentaldata alsosuggestsWarburgimpedance
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“Avalanche dynamics” from LFPs in vivo
Petermann et al., PNAS 2009
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Avalanche analysis from LFP activity (awake cat)
Touboul & Destexhe,PLoS One, 2010
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Avalanche analysis from LFP activity (awake cat)
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Avalanche analysis from LFP activity (awake cat)
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Avalanche analysis from LFP activity (awake cat)
Shuffled LFP peaks (random process!)
Touboul & Destexhe,PLoS One, 2010
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Avalanche analysis from LFP activity (awake cat)
Shuffled LFP peaks (random process!)
Touboul & Destexhe,PLoS One, 2010
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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
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Plan
1. Characterization of neuronal activity in theneocortex of awake animals
2. Characterization of LFPs
3. Modeling neuronal activity in awake cortex
![Page 35: Fireflies on the Water How much stochastic is neuronal activity · Synaptic activity is intense and noisy, essentially Gaussian distributed (both for Vm and conductances) Responsible](https://reader033.vdocument.in/reader033/viewer/2022060507/5f1fc7265da73b379206e7d6/html5/thumbnails/35.jpg)
Network models of self-sustained irregular states
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Network models of asynchronous irregular states
Brunel, J Physiol Paris, 2000
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Self-sustained asynchronous irregular states
Vogels & Abbott,J Neurosci 2005
El Boustani & Destexhe,Neural Computation 2009
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Analysis of AI states
El Boustani et al.,J Physiol Paris, 2007
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Analysis of AI states
El Boustani et al.,J Physiol Paris, 2007
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Analysis of AI states
El Boustani et al.,J Physiol Paris, 2007
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Analysis of AI states
El Boustani et al.,J Physiol Paris, 2007
20 timestoo many!
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Modulation of information transfer by network activity
How to obtain models consistentwith conductance measurements ?
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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
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Mean-field model of AI states
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Mean-field model of AI states
Model predictionNumerical simulation Difference
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Conductancemaps
Mean-field model of AI states
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Network models with realistic conductance patternsBest model: N=16000, 320 synapses/neuron
Vogels & Abbott, J Neurosci, 2005
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Comparison
Network models with realistic conductance patterns
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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
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Thanks to the team...Michelle Rudolph
MartinPospischilSami
El BoustaniClaudeBedard
OlivierMarre
JonathanTouboul
SerafimRodrigues
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