Model based decoding of spike trains.

Matthew C Wiener; Barry J Richmond

doi:10.1016/s0303-2647(02)00087-4

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PubMed · 12459310

Model based decoding of spike trains.

Matthew C Wiener·Barry J Richmond

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Abstract

Reliably decoding neuronal responses requires knowing what aspects of neuronal responses are stimulus related, and which aspects act as noise. Recent work shows that spike trains can be viewed as stochastic samples from the rate variation function, as estimated by the time dependent spike density function (or normalized peristimulus time histogram). Such spike trains are exactly described by order statistics, and can be decoded millisecond-by-millisecond by iterative application of order statistics.

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Matthew C Wiener, Barry J Richmond. Model based decoding of spike trains.. https://doi.org/10.1016/s0303-2647(02)00087-4

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