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Optimal decision making on the basis of evidence represented in spike trains

Zhang, Jiaxiang and Bogacz, Rafal 2010. Optimal decision making on the basis of evidence represented in spike trains. Neural Computation 22 (5) , pp. 1113-1148. 10.1162/neco.2009.05-09-1025

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Abstract

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Publisher: MIT Press
ISSN: 0899-7667
Last Modified: 04 Jun 2017 07:52
URI: http://orca-mwe.cf.ac.uk/id/eprint/69147

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