Literature DB >> 16942856

Computational algorithms and neuronal network models underlying decision processes.

Yutaka Sakai1, Hiroshi Okamoto, Tomoki Fukai.   

Abstract

Animals or humans often encounter such situations in which they must choose their behavioral responses to be made in the near or distant future. Such a decision is made through continuous and bidirectional interactions between the environment surrounding the brain and its internal state or dynamical processes. Therefore, decision making may provide a unique field of researches for studying information processing by the brain, a biological system open to information exchanges with the external world. To make a decision, the brain must analyze pieces of information given externally, past experiences in a similar situation, possible behavioral responses, and predicted outcomes of the individual responses. In this article, we review results of recent experimental and theoretical studies of neuronal substrates and computational algorithms for decision processes.

Entities:  

Mesh:

Year:  2006        PMID: 16942856     DOI: 10.1016/j.neunet.2006.05.034

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  7 in total

1.  Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations.

Authors:  Mark A Bourjaily; Paul Miller
Journal:  J Neurophysiol       Date:  2012-03-28       Impact factor: 2.714

2.  Information: theory, brain, and behavior.

Authors:  Greg Jensen; Ryan D Ward; Peter D Balsam
Journal:  J Exp Anal Behav       Date:  2013-10-04       Impact factor: 2.468

3.  A dynamic neural field model of continuous input integration.

Authors:  Weronika Wojtak; Stephen Coombes; Daniele Avitabile; Estela Bicho; Wolfram Erlhagen
Journal:  Biol Cybern       Date:  2021-08-21       Impact factor: 2.086

4.  Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits.

Authors:  Paul Miller; Donald B Katz
Journal:  J Comput Neurosci       Date:  2013-04-23       Impact factor: 1.621

5.  Robustness of learning that is based on covariance-driven synaptic plasticity.

Authors:  Yonatan Loewenstein
Journal:  PLoS Comput Biol       Date:  2008-03-07       Impact factor: 4.475

6.  Melioration Learning in Two-Person Games.

Authors:  Johannes Zschache
Journal:  PLoS One       Date:  2016-11-16       Impact factor: 3.240

7.  When does reward maximization lead to matching law?

Authors:  Yutaka Sakai; Tomoki Fukai
Journal:  PLoS One       Date:  2008-11-24       Impact factor: 3.240

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.