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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains

Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains

Chapter:
(p.71) 4 Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains
Source:
Bayesian Brain
Author(s):

Barry J. Richmond

Matthew C. Wiener

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0004

This chapter, which discusses the application of order statistics with Bayes theorem in decoding instant-by-instant neuronal spike trains, first considers the response latency or the time at which the neuronal response to a stimulus begins. Statistics are then developed to decode the responses based on the first spike and then consider each subsequent spike as the “first next spike.” Order statistics describe the distribution of a spike time when the total number of spikes is known. The probability of the stimulus given the outcome of whether or not a spike has arrived can be determined using the Bayes theorem.

Keywords:   order statistics, Bayes theorem, response latency, neuronal response, spike

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