How much stochastic is neuronal activity ? Alain Destexhe Unit de - - PowerPoint PPT Presentation

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How much stochastic is neuronal activity ? Alain Destexhe Unit de - - PowerPoint PPT Presentation

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Yayoyi Kusama, Fireflies on the Water How much stochastic is neuronal activity ? Alain Destexhe Unit de Neurosciences, Information et Complexit (UNIC) CNRS Gif-sur-Yvette, France http://cns.iaf.cnrs-gif.fr Contributors: Theory: Claude

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Unité de Neurosciences, Information et Complexité (UNIC) CNRS Gif-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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Wessberg Crist & Nicolelis (2002)

Ensemble activity in the cortex of a behaving rhesus monkey

Neuronal activity in awake monkey

Complex spatiotemporal patterns of neuronal discharges

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Plan

  • 1. Characterization of neuronal activity in the

neocortex 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. 1993 Bedard, 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 Reviews

  • Neurosci. 2003
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Conductance measurements in vivo

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

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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

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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
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Characterization of up-states in vivo

Destexhe & Rudolph Neuronal 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 measurements in 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 & Rudolph Neuronal Noise (data from Hasenstaub & McCormick)

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Characterization of up-states in vitro

Destexhe & Rudolph Neuronal Noise (data from Hasenstaub & McCormick)

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Characterization of up-states in vitro

Destexhe & Rudolph Neuronal Noise (data from Hasenstaub & McCormick)

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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)

Characterizing neuronal activity

Destexhe & Rudolph, Neuronal Noise, Springer 2010

Conclusions

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Plan

  • 1. Characterization of neuronal activity in the

neocortex 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 diffusion in homogeneous medium 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 & Destexhe Submitted

Fitting different transfer functions to experimental data also suggests Warburg impedance

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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 also be explained by thresholding procedure Similar to neuronal activity, a lot can be explained by purely stochastic mechanisms...

Characterizing LFP activity Conclusions

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Plan

  • 1. Characterization of neuronal activity in the

neocortex of awake animals

  • 2. Characterization of LFPs
  • 3. Modeling neuronal activity in awake cortex
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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 times too many!

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Modulation of information transfer by network activity

How to obtain models consistent with conductance measurements ?

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El Boustani & Destexhe, Neural Computation 2009

Mean-field model of AI states

Macroscopic modeling of AI states in spiking networks Optical imaging 1 pixel = network of randomly-connected neurons

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Mean-field model of AI states

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Mean-field model of AI states

Model prediction Numerical simulation Difference

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Conductance maps Mean-field model of AI states

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Network models with realistic conductance patterns

Best 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 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...)

Modeling the awake neocortex

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Thanks to the team... Michelle Rudolph Martin Pospischil Sami El Boustani Claude Bedard Olivier Marre Jonathan Touboul Serafim Rodrigues

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