SLIDE 1 Unité de Neurosciences, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette http://cns.iaf.cnrs-gif.fr
Synaptic noise and its consequences
- n the integrative properties of cortical neurons
Le bruit synaptique et ses conséquences sur les propriétés intégratives des neurones corticaux
Alain Destexhe
(Courtesy of Alex Thomson, University of London, UK) Institut de Neurobiologie Alfred Fessard, CNRS, Gif sur Yvette
SLIDE 2 Wessberg Crist & Nicolelis (2002)
Ensemble activity in the cortex of a behaving rhesus monkey
Complex and seemingly stochastic patterns of neuronal discharge
SLIDE 3 Multiscale analysis
Characterization of “noisy” network activity in vivo: High-conductance states
EEG Units
Integrative properties of single neurons during High-Conductance states High-conductance states at the network level
SLIDE 4
PLAN
How stochastic is neuronal activity ?
SLIDE 5 Human ensemble recordings Utah-array recordings
Peyrache et al, PNAS, 2012
SLIDE 6 Human ensemble recordings
Peyrache et al, PNAS, 2012
RS/FS cells monosynaptic connections
SLIDE 7 Human ensemble recordings
Peyrache et al, PNAS, 2012
RS/FS correlations
SLIDE 8 Human ensemble recordings
Peyrache et al, PNAS, 2012
RS/FS correlations
SLIDE 9 Multiunit extracellular recordings in awake cats
Softky & Koch, J Neurosci. 1993 Bedard et al., Phys Rev Lett 2006
Apparent stochastic dynamics!
SLIDE 10 Multiunit extracellular recordings in awake cats
Marre et al., Physical Review Letters, 2009
Correlated
Statistics of spike patterns in cat parietal cortex
Uncorrelated
SLIDE 11
PLAN
High-conductance states
SLIDE 12 Intracellular characterization of network activity in vivo
(Courtesy of Igor Timofeev, Laval University, Canada)
Intracellular recordings in parietal cortex
- f awake and sleeping cats
SLIDE 13 Synaptic “noise” in vivo
Pare et al. J Neurophysiol. 1998 Steriade et al. J Neurophysiol. 2001 Destexhe et al. Nature Reviews
Intracellular recordings in parietal cortex in different brain states
SLIDE 14 Conductance measurements in vivo
Paré et al., J. Neurophysiol. 1998 Destexhe et al., Nature Reviews Neurosci. 2003
SLIDE 15 Characterization of up-states in vivo by TTX microdialysis
Microperfusion of TTX in cat parietal cortex under ketamine-xylazine anesthesia
Paré et al., J. Neurophysiol. 1998 Destexhe et al., Nature Reviews Neurosci. 2003
SLIDE 16 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
Characterizing neuronal activity
Destexhe & Rudolph, Neuronal Noise, Springer 2012
Summary of measurements
in awake animals
SLIDE 17
PLAN
Modeling high-conductance states in cortical neurons
SLIDE 18 Detailed models of HC states
Reconstructed neocortical pyramidal neurons with synaptic densities estimated from morphological measurements
Total synapses: 16% inhibitory 84% excitatory Spine density: (dendrites > 40 µm from soma) 0.6 spines per µm2 GABAergic synapses on the soma: 10.6 ± 3.7 per 100 µm2 Total GABAergic synapses: 7% on soma 93% in dendrites DeFelipe & Fariñas, Prog. Neurobiol. (1992); Larkman, Comp. Neurol. (1991)
SLIDE 19
- 1. Calibration of the model to
miniature synaptic events recorded intracellularly in vivo
rates to active states recorded intracellularly in vivo => Rin, <Vm>, σV
Detailed models of HC states
SLIDE 20
PLAN
Simplified models of high-conductance states
SLIDE 21
Global synaptic conductances
SLIDE 22 The “point-conductance” model
Simplified representation of synaptic background activity as a random-walk process [Uhlenbeck & Ornstein (1930)]
Destexhe et al., Neuroscience 2001
Simplifed models of HC states
SLIDE 23
PLAN
Consequences of high-conductance states in cortical neurons
SLIDE 24 Consequence 1: neurons are probabilistic devices
Ho & Destexhe, J Neurophysiol. 2000
SLIDE 25
Consequence 2: Enhanced responsiveness
Quiescent High-conductance noise
SLIDE 26
Enhanced responsiveness
SLIDE 27 Enhanced responsiveness at the network level
Synaptic background activity enhances the detection of synaptic inputs at the network level
Ho & Destexhe, J Neurophysiol. 2000
SLIDE 28 Consequence 3: Equalization of synaptic efficacies
Location independence of cellular response to synaptic stimulation
Rudolph & Destexhe, J. Neurosci. 2003
SLIDE 29 EPSP attenuation during high-conductance states
Destexhe et al., Nature Reviews Neuroscience 2003
SLIDE 30 EPSP attenuation during high-conductance states
Destexhe et al., Nature Reviews Neuroscience 2003
SLIDE 31 EPSP attenuation during high-conductance states
Destexhe et al., Nature Reviews Neuroscience 2003
SLIDE 32 Location independence in different cellular morphologies
Equalization of synaptic efficacy
Rudolph & Destexhe, J. Neurosci. 2003
SLIDE 33 Reconstruction of location independence from the probabilities
- f AP initiation and propagation
Q (AP propagation) PQ P (AP initiation)
Equalization of synaptic efficacy
Rudolph & Destexhe, J. Neurosci. 2003
SLIDE 34 Reconstruction of location independence from the probabilities
- f AP initiation and propagation
Equalization of synaptic efficacy
Rudolph & Destexhe, J. Neurosci. 2003
probability for evoking a dendritic AP probability of evoking a soma/axon AP probability that a dendritic AP leads to soma/axon AP
x =
SLIDE 35 Consequence 4: Sharper temporal resolution
Destexhe et al., Nature Reviews Neurosci. 2003
SLIDE 36 The non-linear properties of thalamocortical cells
Hyperpolarization
Low threshold Ca 2+(IT)
Consequence 5: noise modulates intrinsic properties
Wolfart et al., Nature Neurosci, 2005
SLIDE 37
PLAN
Recreating high-conductance states in cortical neurons in vitro
SLIDE 38 Interaction between Models and Living Cells
“Recreating synaptic noise”: Real-time injection of stochastic synaptic conductances (dynamic-clamp)
g (t)
e
g (t)
i
V (t)
m
SLIDE 39 The Dynamic-clamp
I = g(t) (V - E )
inj biol rev
Iinj Vbiol g(t)
Robinson & Kawai, 1993 Sharp et al., 1993
SLIDE 40 The Dynamic-clamp
Iinj Vbiol g(t)
RT-NEURON
RT-NEURON is developed by Gwen LeMasson, University of Bordeaux
SLIDE 41
”Recreation” of in vivo-like activity by injection of fluctuating conductances under dynamic-clamp
Point-conductance models of SBA
SLIDE 42 Point-conductance models of SBA
Destexhe et al., Neuroscience 2001
”Recreation” of in vivo-like activity by injection of fluctuating conductances under dynamic-clamp
SLIDE 43 Point-conductance models of SBA
Destexhe et al., Neuroscience 2001; Rudolph et al., J Neurophysiol 2004
Natural up state Artificial up state ”Recreation” of in vivo-like activity by injection of fluctuating conductances under dynamic-clamp
SLIDE 44
Extracting conductances from in vivo activity
SLIDE 45 Rudolph, Pospischil, Timofeev & Destexhe, J. Neurosci, 2007
Conductance measurements in awake cats
Extracting conductances from in vivo activity
Excitatory and inhibitory conductances
SLIDE 46
Contrasting low and high conductance states
Low-conductance states (excitation ~ inhibition) High-conductance states (inhibition >> excitation)
SLIDE 47
Spike-triggered averages of conductances
Dynamic-clamp
SLIDE 48 Spike-triggered variances of conductances
Rudolph, et al.,
SLIDE 49 Destexhe, Current Opin. Neurobiol., 2011
Spike-triggered averages of conductances
SLIDE 50
PLAN
Conductance measurements for sensory-evoked responses
SLIDE 51
Excitation Inhibition
Thalamocortical loops
SLIDE 52 Auditory cortex
Wehr & Zador, Nature, 2003
SLIDE 53 Somatosensory cortex
Wilent & Contreras, Nat Neurosci, 2005
SLIDE 54 Wilent & Contreras, Nat Neurosci, 2005
Somatosensory cortex
SLIDE 55
PLAN
How to reconcile these results ?
SLIDE 56 Brunel, J Physiol Paris, 2000 Vogels & Abbott, J Neurosci 2005 El Boustani et al., J Physiol Paris, 2007 Destexhe, Current Opin. Neurobiol., 2011
Networks of IF neurons
STA analysis in models
SLIDE 57 Destexhe, Current Opinion Neurobiol., 2011
STA analysis in models
Internal activity External input
SLIDE 58
Excitation Inhibition Inhibition Excitation
Interpretation
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g
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g
Sensory or external input Internal (recurrent) activity
SLIDE 59 Stochastic analysis of Vm fluctuations reveals dominant inhibitory conductances Two ways to evoke spikes: by excitation (rare)
- r release of inhibition (more generally seen)
Spikes in awake state are essentially evoked by internal activity rather than being evoked by external inputs
Stochastic analysis of single cortical neurons in vivo Summary of the stochastic analysis of High-conductance States
SLIDE 60
Review material (from our lab), available on http://cns.iaf.cnrs-gif.fr (in “Publications”) Scholarpedia article on "High-conductance states" (open access; many articles available, such as “dynamic-clamp”, “neuronal noise”, etc) Destexhe et al. “High-conductance states”, Nature Reviews Neuroscience 2003 Destexhe, Current Opinion Neurobiology, 2011
Reading material
Inhibition Excitation
SLIDE 61
2009 2012
