Hostname: page-component-6898cfcb55-cbk24 Total loading time: 0 Render date: 2026-11-11T18:11:22.470Z Has data issue: false hasContentIssue false

A neural-network interpretation of selection in learning and behavior

Published online by Cambridge University Press:  06 November 2001

José E Burgos
Affiliation:
University of Guadalajara, Col. Chapalita, Guadalajara, Jalisco 45030, Mexico jburgos@cucba.udg.mx www.udgserv.cencar.udg.mx/~ceip/
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

In their account of learning and behavior, the authors definean interactor as emitted behavior that operates on the environment, whichexcludes Pavlovian learning. A unified neural-network account of theoperant-Pavlovian dichotomy favors interpreting neurons as interactors and synaptic efficacies as replicators. The latter interpretation implies that single-synapse change is inherently Lamarckian.

Information

Type
Brief Report
Copyright
© 2001 Cambridge University Press