Feature selection in simple neurons: how coding depends on spiking dynamics

Details Feature selection in simple neurons: how coding depends on spiking dynamics Feature selection in simple neurons: how coding depends on spiking dynamics

2009-02-12

arxiv.org/abs/0902.2020v3

Neural Computation March 2010, Vol. 22, No. 3: 581-598

Biology

Quantitative Biology

Neurons and Cognition

23 Pages, LaTeX + 4 Figures. v2 is substantially expanded and revised. v3 corrects minor errors in Sec. 3.6

Scientific paper

10.1162/neco.2009.02-09-956

The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes, and a nonlinear function that relates stimulus to firing probability. In many sensory systems, these two components of the coding strategy are found to adapt to changes in the statistics of the inputs, in such a way as to improve information transmission. Here, we show for two simple neuron models how feature selectivity as captured by the spike-triggered average depends both on the parameters of the model and on the statistical characteristics of the input.

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