Abstract:
:We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes only the neuron characteristics and the other of which describes only the signal characteristics. The approximation shows particularly clearly that signal autocorrelations and cross-correlations arise as natural features of the interspike-interval density and are particularly clear for small signals and moderate noise. We show that this model simplifies the design of spiking neuron cross-correlation systems and describe a four-neuron mutual inhibition network that generates a cross-correlation output for two input signals.
journal_name
Neural Computjournal_title
Neural computationauthors
Tapson J,Jin C,van Schaik A,Etienne-Cummings Rdoi
10.1162/neco.2009.06-07-548subject
Has Abstractpub_date
2009-06-01 00:00:00pages
1554-88issue
6eissn
0899-7667issn
1530-888Xpii
10.1162/neco.2009.06-07-548journal_volume
21pub_type
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