Activity Driven Adaptive Stochastic Resonance

Cortical neurons in vivo show fluctuations in their membrane potential of the order of several milli-volts. Using simple and biophysically realistic models of a single neuron we demonstrate that noise induced fluctuations can be used to adaptively optimize the sensitivity of the neuron's output to ensembles of subthreshold inputs of different average strengths. Optimal information transfer is achieved by changing the strength of the noise such that the neuron's average firing rate remains constant. Adaptation is fast, because only crude estimates of the output rate are required at any time.

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