Optimal coding of a random stimulus by a population of parallel neuron models

Mark D. McDonnell; Nigel G. Stocks; Derek Abbott

doi:10.1117/12.724618

15 June 2007 Optimal coding of a random stimulus by a population of parallel neuron models

Mark D. McDonnell, Nigel G. Stocks, Derek Abbott

Proceedings Volume 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems; 66020R (2007) https://doi.org/10.1117/12.724618
Event: SPIE Fourth International Symposium on Fluctuations and Noise, 2007, Florence, Italy

Abstract

We examine the question of how a population of independently noisy sensory neurons should be configured to optimize the encoding of a random stimulus into sequences of neural action potentials. For the case where firing rates are the same in all neurons, we consider the problem of optimizing the noise distribution for a known stimulus distribution, and the converse problem of optimizing the stimulus for a given noise distribution. This work is related to suprathreshold stochastic resonance (SSR). It is shown that, for a large number of neurons, the SSR model is equivalent to a single rate-coding neuron with multiplicative output noise.

Citation Download Citation

Mark D. McDonnell, Nigel G. Stocks, and Derek Abbott "Optimal coding of a random stimulus by a population of parallel neuron models", Proc. SPIE 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, 66020R (15 June 2007); https://doi.org/10.1117/12.724618

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