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: Theory: Claude Bedard, Sami El Boustani, Olivier Marre, Serafim Rodrigues, Michelle Rudolph (UNIC), Experiments: Diego Contreras (U Penn, USA), Igor Timofeev, FACETS Mircea Steriade (Laval University, Canada) (EU IST)
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 Wake: Poisson: VLC media file Data: Destexhe, Contreras & Steriade, J. Neurosci. 1999 VLC media file (.mp3) (.mp3) Music: http://www.archive.org/details/NeuronalTones
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 1 pixel = network of Optical imaging 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 Jonathan Serafim Touboul Rodrigues Martin Claude Pospischil Sami Bedard El Boustani Olivier Marre
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