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Adjusting Stochastic Resonance in a Leaky Integrate and Fire Neuron to Sub-threshold Stimulus Distributions
Citation key Wenning2002
Author Wenning, G. and Obermayer, K.
Pages 225 – 231
Year 2002
DOI 10.1016/S0925-2312(02)00437-X
Journal Neurocomputing
Volume 44-46
Publisher Elsevier
Abstract Here we study in an abstract model how a single neuron could adapt its properties to maximize information processing capabilities in case of weak signal input and additional noise, the natural realm of stochastic resonance. The dynamics of the membrane potential is described by an Ornstein–Uhlenbeck process in a hazard function approximation. First we analytically and numerically characterize the effect of stochastic resonance as a function of the model's parameters. Then we derive an activity-dependent learning rule for the adjustment of the noise inputs and show that it only depends on quantities which could be estimated locally by the neuron.
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