2008
DOI: 10.1103/physrevlett.100.118103
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Novel Class of Neural Stochastic Resonance and Error-Free Information Transfer

Abstract: We investigate a novel class of neural stochastic resonance (SR) exhibiting error-free information transfer. Unlike conventional neural SR, where the decrease of a system's response with too much noise is associated with an increase in the baseline firing rate, here the bell-shaped SR behavior of the input-output cross correlation emerges versus increasing input noise in spite of no significant increase of the baseline firing rate. The neuron thus acts as an error-free detector for weak signals. An integrate-a… Show more

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Cited by 40 publications
(54 citation statements)
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“…In order to quantify the level of coherence between the input signal and the response of the postsynaptic neuron, we employed a cross-correlation function defined as in [32], that is,where is the total recording time of each trial, typically much greater than the signal period , and is the instantaneous firing rate of the postsynaptic neuron. This type of cross-correlation functions have been extensively used in the literature to measure the input-output dependence in neuron models and experiments (see, for instance, [11], [32], [33]). An example of stochastic resonance in the case of a presynaptic population with static synapses is shown in figure 2.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In order to quantify the level of coherence between the input signal and the response of the postsynaptic neuron, we employed a cross-correlation function defined as in [32], that is,where is the total recording time of each trial, typically much greater than the signal period , and is the instantaneous firing rate of the postsynaptic neuron. This type of cross-correlation functions have been extensively used in the literature to measure the input-output dependence in neuron models and experiments (see, for instance, [11], [32], [33]). An example of stochastic resonance in the case of a presynaptic population with static synapses is shown in figure 2.…”
Section: Resultsmentioning
confidence: 99%
“…In the brain, it has been found in different types of sensory neurons [8], [9], in the hippocampus [10], in the brain stem [11], and in some cortical areas [12][15]. Although SR behavior has been extensively studied in many works, most of them assume a controlled source of noise that affects the dynamics of the system additively and, in some cases, without temporal correlations.…”
Section: Introductionmentioning
confidence: 99%
“…Several neural network models have been constructed to produce SR-like effects without an explicit multistable potential or a simple additive noise term [62][65]. For example, Mejias and Longtin [62] presented a heterogeneous spiking neuron network in which the average firing rate of the network is modulated by a weak, periodic input signal.…”
Section: Discussionmentioning
confidence: 99%
“…As for noise property necessary necessary for SR, it is known that not only white noise but also temporally or spatially colored noise can induce SR [21] [23][20] [6]. Furthermore, SR is observable even when noise magnitude is too strong [4], and when signals are supra-threshold [10] [25].…”
Section: B Stochastic Resonancementioning
confidence: 99%