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- G. Wenning and
K. Obermayer. Activity Driven Adaptive Stochastic Resonance.
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In Advances in Neural Information Processing Systems 14, pages
301-308, Cambridge, Massachusetts, 2002. MIT Press.
(FTP Gzipped PostScript, 8 pages, 88 kb)
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 neurons
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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