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Models of Neural Systems

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Activity Driven Adaptive Stochastic Resonance
Citation key Wenn2002a
Author Wenning, G. and Obermayer, K.
Title of Book Advances in Neural Information Processing Systems 14
Pages 301 – 308
Year 2002
Publisher MIT Press
Abstract Cortical neurons might be considered as threshold elements integrating in parallel many excitatory and inhibitory inputs. Due to the apparent variability of cortical spike trains this yields a strongly fluctuating membrane potential, such that threshold crossings are highly irregular. Here we study how a neuron could maximize its sensitivity w.r.t. a relatively small subset of excitatory input. Weak signals embedded in fluctuations is the natural realm of stochastic resonance. The neuron\?s response is described on a hazard-function approximation applied to an Ornstein-Uhlenbeck process. We analytically derive an optimality criterium and give a learning rule for the adjustment of the membrane fluctuations, such that the sensiivity is maximal exploiting st We show that adaptation depends only on quantities that could easily be estimated locally (in space and time) by the neuron. The main results are compared with simulations of a biophysically more realistic neuron model.
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