2,145 research outputs found
Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
We present two Bayesian procedures to infer the interactions and external
currents in an assembly of stochastic integrate-and-fire neurons from the
recording of their spiking activity. The first procedure is based on the exact
calculation of the most likely time courses of the neuron membrane potentials
conditioned by the recorded spikes, and is exact for a vanishing noise variance
and for an instantaneous synaptic integration. The second procedure takes into
account the presence of fluctuations around the most likely time courses of the
potentials, and can deal with moderate noise levels. The running time of both
procedures is proportional to the number S of spikes multiplied by the squared
number N of neurons. The algorithms are validated on synthetic data generated
by networks with known couplings and currents. We also reanalyze previously
published recordings of the activity of the salamander retina (including from
32 to 40 neurons, and from 65,000 to 170,000 spikes). We study the dependence
of the inferred interactions on the membrane leaking time; the differences and
similarities with the classical cross-correlation analysis are discussed.Comment: Accepted for publication in J. Comput. Neurosci. (dec 2010
Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
We investigate the effect of electric synapses (gap junctions) on collective
neuronal dynamics and spike statistics in a conductance-based
Integrate-and-Fire neural network, driven by a Brownian noise, where
conductances depend upon spike history. We compute explicitly the time
evolution operator and show that, given the spike-history of the network and
the membrane potentials at a given time, the further dynamical evolution can be
written in a closed form. We show that spike train statistics is described by a
Gibbs distribution whose potential can be approximated with an explicit
formula, when the noise is weak. This potential form encompasses existing
models for spike trains statistics analysis such as maximum entropy models or
Generalized Linear Models (GLM). We also discuss the different types of
correlations: those induced by a shared stimulus and those induced by neurons
interactions.Comment: 42 pages, 1 figure, submitte
Origins of choice-related activity in mouse somatosensory cortex.
During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons
How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation
This paper addresses two questions in the context of neuronal networks
dynamics, using methods from dynamical systems theory and statistical physics:
(i) How to characterize the statistical properties of sequences of action
potentials ("spike trains") produced by neuronal networks ? and; (ii) what are
the effects of synaptic plasticity on these statistics ? We introduce a
framework in which spike trains are associated to a coding of membrane
potential trajectories, and actually, constitute a symbolic coding in important
explicit examples (the so-called gIF models). On this basis, we use the
thermodynamic formalism from ergodic theory to show how Gibbs distributions are
natural probability measures to describe the statistics of spike trains, given
the empirical averages of prescribed quantities. As a second result, we show
that Gibbs distributions naturally arise when considering "slow" synaptic
plasticity rules where the characteristic time for synapse adaptation is quite
longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure
Evidence for an additive inhibitory component of contrast adaptation
The latency of visual responses generally decreases as contrast increases.
Recording in the lateral geniculate nucleus (LGN), we find that response
latency increases with increasing contrast in ON cells for some visual stimuli.
We propose that this surprising latency trend can be explained if ON cells rest
further from threshold at higher contrasts. Indeed, while contrast changes
caused a combination of multiplicative gain change and additive shift in LGN
cells, the additive shift predominated in ON cells. Modeling results supported
this theory: the ON cell latency trend was found when the distance-to-threshold
shifted with contrast, but not when distance-to-threshold was fixed across
contrasts. In the model, latency also increases as surround-to-center ratios
increase, which has been shown to occur at higher contrasts. We propose that
higher-contrast full-field stimuli can evoke more surround inhibition, shifting
the potential further from spiking threshold and thereby increasing response
latency
Statistics of spike trains in conductance-based neural networks: Rigorous results
We consider a conductance based neural network inspired by the generalized
Integrate and Fire model introduced by Rudolph and Destexhe. We show the
existence and uniqueness of a unique Gibbs distribution characterizing spike
train statistics. The corresponding Gibbs potential is explicitly computed.
These results hold in presence of a time-dependent stimulus and apply therefore
to non-stationary dynamics.Comment: 42 pages, 1 figure, to appear in Journal of Mathematical Neuroscienc
Universal properties of correlation transfer in integrate-and-fire neurons
One of the fundamental characteristics of a nonlinear system is how it
transfers correlations in its inputs to correlations in its outputs. This is
particularly important in the nervous system, where correlations between
spiking neurons are prominent. Using linear response and asymptotic methods for
pairs of unconnected integrate-and-fire (IF) neurons receiving white noise
inputs, we show that this correlation transfer depends on the output spike
firing rate in a strong, stereotyped manner, and is, surprisingly, almost
independent of the interspike variance. For cells receiving heterogeneous
inputs, we further show that correlation increases with the geometric mean
spiking rate in the same stereotyped manner, greatly extending the generality
of this relationship. We present an immediate consequence of this relationship
for population coding via tuning curves
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