18,824 research outputs found
The Spatial Structure of Stimuli Shapes the Timescale of Correlations in Population Spiking Activity
Throughout the central nervous system, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus structure and behavioral context. Such shaping is thought to underlie important changes in the neural code, but the neural circuitry responsible is largely unknown. In this study, we investigate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of weakly electric fish. Simultaneous single unit recordings of principal electrosensory cells show that an increase in the spatial extent of stimuli increases correlations at short (~10 ms) timescales while simultaneously reducing correlations at long (~100 ms) timescales. A spiking network model of the first two stages of electrosensory processing replicates this correlation shaping, under the assumptions that spatially broad stimuli both saturate feedforward afferent input and recruit an open-loop inhibitory feedback pathway. Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. For weak stimuli, linear response analysis of the spiking network shows that the reduction of long timescale correlation for spatially broad stimuli is similar to correlation cancellation mechanisms previously suggested to be operative in mammalian cortex. The mechanism for correlation shaping supports population-level filtering of irrelevant distractor stimuli, thereby enhancing the population response to relevant prey and conspecific communication inputs. © 2012 Litwin-Kumar et al
Spike trains statistics in Integrate and Fire Models: exact results
We briefly review and highlight the consequences of rigorous and exact
results obtained in \cite{cessac:10}, characterizing the statistics of spike
trains in a network of leaky Integrate-and-Fire neurons, where time is discrete
and where neurons are subject to noise, without restriction on the synaptic
weights connectivity. The main result is that spike trains statistics are
characterized by a Gibbs distribution, whose potential is explicitly
computable. This establishes, on one hand, a rigorous ground for the current
investigations attempting to characterize real spike trains data with Gibbs
distributions, such as the Ising-like distribution, using the maximal entropy
principle. However, it transpires from the present analysis that the Ising
model might be a rather weak approximation. Indeed, the Gibbs potential (the
formal "Hamiltonian") is the log of the so-called "conditional intensity" (the
probability that a neuron fires given the past of the whole network). But, in
the present example, this probability has an infinite memory, and the
corresponding process is non-Markovian (resp. the Gibbs potential has infinite
range). Moreover, causality implies that the conditional intensity does not
depend on the state of the neurons at the \textit{same time}, ruling out the
Ising model as a candidate for an exact characterization of spike trains
statistics. However, Markovian approximations can be proposed whose degree of
approximation can be rigorously controlled. In this setting, Ising model
appears as the "next step" after the Bernoulli model (independent neurons)
since it introduces spatial pairwise correlations, but not time correlations.
The range of validity of this approximation is discussed together with possible
approaches allowing to introduce time correlations, with algorithmic
extensions.Comment: 6 pages, submitted to conference NeuroComp2010
http://2010.neurocomp.fr/; Bruno Cessac
http://www-sop.inria.fr/neuromathcomp
A characterization of the Edge of Criticality in Binary Echo State Networks
Echo State Networks (ESNs) are simplified recurrent neural network models
composed of a reservoir and a linear, trainable readout layer. The reservoir is
tunable by some hyper-parameters that control the network behaviour. ESNs are
known to be effective in solving tasks when configured on a region in
(hyper-)parameter space called \emph{Edge of Criticality} (EoC), where the
system is maximally sensitive to perturbations hence affecting its behaviour.
In this paper, we propose binary ESNs, which are architecturally equivalent to
standard ESNs but consider binary activation functions and binary recurrent
weights. For these networks, we derive a closed-form expression for the EoC in
the autonomous case and perform simulations in order to assess their behavior
in the case of noisy neurons and in the presence of a signal. We propose a
theoretical explanation for the fact that the variance of the input plays a
major role in characterizing the EoC
Dreaming neural networks: forgetting spurious memories and reinforcing pure ones
The standard Hopfield model for associative neural networks accounts for
biological Hebbian learning and acts as the harmonic oscillator for pattern
recognition, however its maximal storage capacity is , far
from the theoretical bound for symmetric networks, i.e. . Inspired
by sleeping and dreaming mechanisms in mammal brains, we propose an extension
of this model displaying the standard on-line (awake) learning mechanism (that
allows the storage of external information in terms of patterns) and an
off-line (sleep) unlearningconsolidating mechanism (that allows
spurious-pattern removal and pure-pattern reinforcement): this obtained daily
prescription is able to saturate the theoretical bound , remaining
also extremely robust against thermal noise. Both neural and synaptic features
are analyzed both analytically and numerically. In particular, beyond obtaining
a phase diagram for neural dynamics, we focus on synaptic plasticity and we
give explicit prescriptions on the temporal evolution of the synaptic matrix.
We analytically prove that our algorithm makes the Hebbian kernel converge with
high probability to the projection matrix built over the pure stored patterns.
Furthermore, we obtain a sharp and explicit estimate for the "sleep rate" in
order to ensure such a convergence. Finally, we run extensive numerical
simulations (mainly Monte Carlo sampling) to check the approximations
underlying the analytical investigations (e.g., we developed the whole theory
at the so called replica-symmetric level, as standard in the
Amit-Gutfreund-Sompolinsky reference framework) and possible finite-size
effects, finding overall full agreement with the theory.Comment: 31 pages, 12 figure
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