Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-15T10:38:31.983Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic thresholds for controlling encoding and retrieval operations in localist (or distributed) neural networks: The need for biologically plausible implementations

Published online by Cambridge University Press:  30 August 2019

Alan D. Pickering
Alan D. Pickering
Affiliation:
Department of Psychology, St. George's Hospital Medical School, University of London, London SW17 0RE, United Kingdoma.pickering@sghms.ac.ukwww.sghms.ac.uk/depts/psychology/aphome.htm
Get access Rights & Permissions [Opens in a new window]

Abstract

A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page's general localist framework. This commentary addresses various issues surrounding biologically plausible implementations for such thresholds. Relevant previous research is noted and the particular difficulties relating to the creation of so-called instance representations are highlighted. It is stressed that these issues also apply to distributed models.

Type
Brief Report
Information
Behavioral and Brain Sciences , Volume 23 , Issue 4 , August 2000 , pp. 488 - 489
Copyright
2000 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)