6,185 research outputs found
Point singularities and suprathreshold stochastic resonance in optimal coding
Motivated by recent studies of population coding in theoretical neuroscience,
we examine the optimality of a recently described form of stochastic resonance
known as suprathreshold stochastic resonance, which occurs in populations of
noisy threshold devices such as models of sensory neurons. Using the mutual
information measure, it is shown numerically that for a random input signal,
the optimal threshold distribution contains singularities. For large enough
noise, this distribution consists of a single point and hence the optimal
encoding is realized by the suprathreshold stochastic resonance effect.
Furthermore, it is shown that a bifurcational pattern appears in the optimal
threshold settings as the noise intensity increases. Fisher information is used
to examine the behavior of the optimal threshold distribution as the population
size approaches infinity.Comment: 11 pages, 3 figures, RevTe
The impact of spike timing variability on the signal-encoding performance of neural spiking models
It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance
Emergence of Synchronous Oscillations in Neural Networks Excited by Noise
The presence of noise in non linear dynamical systems can play a constructive
role, increasing the degree of order and coherence or evoking improvements in
the performance of the system. An example of this positive influence in a
biological system is the impulse transmission in neurons and the
synchronization of a neural network. Integrating numerically the Fokker-Planck
equation we show a self-induced synchronized oscillation. Such an oscillatory
state appears in a neural network coupled with a feedback term, when this
system is excited by noise and the noise strength is within a certain range.Comment: 12 pages, 18 figure
Can intrinsic noise induce various resonant peaks?
We theoretically describe how weak signals may be efficiently transmitted
throughout more than one frequency range in noisy excitable media by kind of
stochastic multiresonance. This serves us here to reinterpret recent
experiments in neuroscience, and to suggest that many other systems in nature
might be able to exhibit several resonances. In fact, the observed behavior
happens in our (network) model as a result of competition between (1) changes
in the transmitted signals as if the units were varying their activation
threshold, and (2) adaptive noise realized in the model as rapid
activity-dependent fluctuations of the connection intensities. These two
conditions are indeed known to characterize heterogeneously networked systems
of excitable units, e.g., sets of neurons and synapses in the brain. Our
results may find application also in the design of detector devices.Comment: 10 pages, 2 figure
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