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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 in 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 sensitivity is
maximal exploiting stochastic resonance. 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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