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The Noisy BrainStochastic Dynamics as a Principle of Brain Function$
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Edmund T. Rolls and Gustavo Deco

Print publication date: 2010

Print ISBN-13: 9780199587865

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780199587865.001.0001

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Probabilistic decision-making

Probabilistic decision-making

Chapter:
(p.139) 5 Probabilistic decision-making
Source:
The Noisy Brain
Author(s):

Edmund T. Rolls

Gustavo Deco

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199587865.003.0005

This chapter shows how an attractor network can model probabilistic decision making. For decision making, the attractor network is trained to have two or more attractor states, each of which corresponds to one of the decisions. Each attractor set of neurons receives a biasing input which corresponds to the evidence in favour of that decision. The model not only shows how probabilistic decision making could be implemented in the brain, but also how the evidence can be accumulated over long periods of time because of the integrating action of the attractor's short-term memory network.

Keywords:   attractor network, probabilistic decision-making, STM, biasing input

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