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| 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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