Literature DB >> 8039358

Foraging for brain stimulation: toward a neurobiology of computation.

C R Gallistel1.   

Abstract

The self-stimulating rat performs foraging tasks mediated by simple computations that use interreward intervals and subjective reward magnitudes to determine stay durations. This is a simplified preparation in which to study the neurobiology of the elementary computational operations that make cognition possible, because the neural signal specifying the value of a computationally relevant variable is produced by direct electrical stimulation of a neural pathway. Newly developed measurement methods yield functions relating the subjective reward magnitude to the parameters of the neural signal. These measurements also show that the decision process that governs foraging behavior divides the subjective reward magnitude by the most recent interreward interval to determine the preferability of an option (a foraging patch). The decision process sets the parameters that determine stay durations (durations of visits to foraging patches) so that the ratios of the stay durations match the ratios of the preferabilities.

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Mesh:

Year:  1994        PMID: 8039358     DOI: 10.1016/0010-0277(94)90026-4

Source DB:  PubMed          Journal:  Cognition        ISSN: 0010-0277


  6 in total

1.  Biography of Charles R. Gallistel.

Authors:  Erik Stemmy
Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-30       Impact factor: 11.205

Review 2.  Decision ecology: foraging and the ecology of animal decision making.

Authors:  David W Stephens
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3.  Dynamical regimes in neural network models of matching behavior.

Authors:  Kiyohito Iigaya; Stefano Fusi
Journal:  Neural Comput       Date:  2013-09-18       Impact factor: 2.026

4.  Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales.

Authors:  Kiyohito Iigaya; Yashar Ahmadian; Leo P Sugrue; Greg S Corrado; Yonatan Loewenstein; William T Newsome; Stefano Fusi
Journal:  Nat Commun       Date:  2019-04-01       Impact factor: 14.919

5.  Entropy-based metrics for predicting choice behavior based on local response to reward.

Authors:  Ethan Trepka; Mehran Spitmaan; Bilal A Bari; Vincent D Costa; Jeremiah Y Cohen; Alireza Soltani
Journal:  Nat Commun       Date:  2021-11-12       Impact factor: 17.694

6.  Bayesian deterministic decision making: a normative account of the operant matching law and heavy-tailed reward history dependency of choices.

Authors:  Hiroshi Saito; Kentaro Katahira; Kazuo Okanoya; Masato Okada
Journal:  Front Comput Neurosci       Date:  2014-03-04       Impact factor: 2.380

  6 in total

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