Yayoyi Kusama, Fireflies on the Water
How much stochastic is neuronal activity ?
Alain Destexhe
Unité de Neurosciences, Information et Complexité (UNIC)
CNRS
Gif-sur-Yvette, France
http://cns.iaf.cnrs-gif.fr
Contributors:
FACETS
(EU IST)
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)
Neuronal activity in awake monkey
Complex spatiotemporal patterns of neuronal discharges
Ensemble activity
in the cortex of a
behaving rhesus
monkey
Wessberg
Crist & Nicolelis
(2002)
Plan
1. Characterization of neuronal activity in the
neocortex of awake animals
2. Characterization of LFPs
3. Modeling neuronal activity in awake cortex
Multisite bipolar LFP recordings
Awake
Destexhe et al., J. Neurosci.,1999
Multisite bipolar LFP recordings
Destexhe et al., J. Neurosci.,1999
Multiunit extracellular recordings in awake cats
Data: Destexhe, Contreras & Steriade, J. Neurosci. 1999
Music: http://www.archive.org/details/NeuronalTones
Wake:
Poisson:
VLC media file
(.mp3)
VLC media file
(.mp3)
Multiunit extracellular recordings in awake cats
Apparent stochastic dynamics!
Softky & Koch, J Neurosci. 1993
Bedard, Kroger & Destexhe, Phys Rev Lett 2006
Multiunit extracellular recordings in awake cats
Apparent stochastic dynamics!
Bedard, Kroger & Destexhe, Phys Rev Lett 2006
Multiunit extracellular recordings in awake cats
Statistics of spike patterns in cat parietal cortex
Uncorrelated
Correlated
Marre, El Boustrani, Fregnac & Destexhe
(Phys Rev Lett, 2009)
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 Reviews
Neurosci. 2003
Conductance measurements in vivo
Paré et al., J. Neurophysiol. 1998
Destexhe et al., Nature Reviews Neurosci. 2003
Characterization of up-states in vivo
Microperfusion of TTX in cat parietal cortex
under ketamine-xylazine anesthesia
Paré et al., J. Neurophysiol. 1998
Destexhe et al., Nature Reviews Neurosci. 2003
Characterization of up-states in vivo
Vm distributions
in different network states
Destexhe & Rudolph
Neuronal Noise
Rudolph et al.
J. Neurophysiol 2005
J. Neurosci. 2007
Characterization of up-states in vivo
Conductance measurements in different network states
Destexhe & Rudolph
Neuronal Noise
Rudolph et al.
J. Neurophysiol 2005
J. Neurosci. 2007
Extracting conductances from in vivo activity
Conductance measurements
in awake cats
Rudolph, Pospischil, Timofeev &
Destexhe, J. Neurosci, 2007
Spike-triggered averages of conductances
Rudolph et al.,
J. Neurosci,
2007
Characterization of up-states in vitro
Destexhe & Rudolph
Neuronal Noise
(data from
Hasenstaub & McCormick)
Characterization of up-states in vitro
Destexhe & Rudolph
Neuronal Noise
(data from
Hasenstaub & McCormick)
Characterization of up-states in vitro
Destexhe & Rudolph
Neuronal Noise
(data from
Hasenstaub & McCormick)
Characterizing neuronal activity
Conclusions
Synaptic activity is intense and noisy,
essentially Gaussian distributed (both
for Vm and conductances)
Responsible for a “high-conductance state”
(3 to 5-fold larger than resting conductance)
Statistics of neuronal activity is very close
to Poisson processes
Importance of inhibition (both for absolute
conductance and for the dynamics of
spike initiation)
Destexhe & Rudolph, Neuronal Noise, Springer 2010
Plan
1. Characterization of neuronal activity in the
neocortex 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
Coulomb’s law:
Electrode
Ionic diffusion
in homogeneous
medium
PSD of the LFP:
Bedard & Destexhe, Biophysical Journal, 2009
Modeling LFPs
Bedard & Destexhe, Biophysical Journal, 2009
Transfer function LFP - Vm activity
Fitting different
transfer
functions to
experimental
data also
suggests
Warburg
impedance
Bedard, Rodrigues,
Roy, Contreras &
Destexhe
Submitted
“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
Characterizing LFP activity
Conclusions
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 also
be explained by thresholding procedure
Similar to neuronal activity, a lot can be explained
by purely stochastic mechanisms...
Plan
1. Characterization of neuronal activity in the
neocortex 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
20 times
too many!
El Boustani et al.,
J Physiol Paris, 2007
Modulation of information transfer by network activity
How to obtain models consistent
with conductance measurements ?
Mean-field model of AI states
Macroscopic modeling of AI states in spiking networks
Optical imaging
1 pixel = network of
randomly-connected neurons
El Boustani & Destexhe,
Neural Computation 2009
Mean-field model of AI states
Mean-field model of AI states
Numerical simulation
Model prediction
Difference
Mean-field model of AI states
Conductance
maps
Network models with realistic conductance patterns
Best model: N=16000, 320 synapses/neuron
Vogels & Abbott, J Neurosci, 2005
Network models with realistic conductance patterns
Comparison
Modeling the awake neocortex
Conclusions
Randomly connected networks of IF neurons
can generate dynamics which reproduce
experimental observations in the awake brain...
... except for conductances measurements!
Mean-field models can be used to identify
network configurations with correct
conductance state (work in progress...)
Thanks to the team...
Michelle Rudolph
Claude
Bedard
Jonathan
Touboul
Sami
El Boustani
Serafim
Rodrigues
Martin
Pospischil
Olivier
Marre