Grandmother cells and localist representations: a review of current thinking

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Abstract

There is now a large literature in neuroscience highlighting how some neurons respond highly selectively to high-level information (e.g. cells that respond to specific faces) and a growing literature in psychology and computer science showing that artificial neural networks often learn highly selective representations. Nevertheless, the vast majority of neuroscientists reject “grandmother cell” theories out of hand, and many psychologists reject localist models based on neuroscience. In this review, I detail some of the conceptual confusions regarding grandmother cells that have contributed to this state of affairs, and review the literature of single-unit recording studies in artificial neural networks that may provide insights into why some neurons respond in a highly selective manner. I then briefly review the contributions from leading theorists in psychology and neuroscience. My hope this special issue contributes to a more productive debate on an important issue that has often been characterised by misunderstandings between disciplines.
Original languageEnglish
Pages (from-to)257-273
Number of pages17
JournalLanguage, Cognition and Neuroscience
Volume32
Issue number3
Early online date27 Jan 2017
DOIs
Publication statusPublished - 16 Mar 2017

Structured keywords

  • Language
  • Cognitive Science

Keywords

  • grandmother cells
  • localist representations
  • distributed representationss
  • neural networks
  • deep networks

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