Abstract:
: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 on both the parameters of the model and the statistical characteristics of the input.
journal_name
Neural Computjournal_title
Neural computationauthors
Famulare M,Fairhall Adoi
10.1162/neco.2009.02-09-956subject
Has Abstractpub_date
2010-03-01 00:00:00pages
581-98issue
3eissn
0899-7667issn
1530-888Xjournal_volume
22pub_type
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