Comparing single-unit recordings taken from a localist model to single-cell recording data: a good match

Michele Gubian, Colin Davis, James Adelman, Jeffrey Bowers

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
288 Downloads (Pure)

Abstract

Single-cell recording studies show that some neurons respond to complex visual information (e.g. words, objects, faces) in a highly selective manner, with individual neurons responding to about 0.5% of presented images. Such data have often been taken as inconsistent with “grandmother cell” theories as well as with localist models in psychology. In particular, it is commonly assumed that units in localist models respond to only one input, resulting in greater levels of selectivity than seen in single-cell results. To test this assumption, we recorded unit activity from a localist model of word identification. Our results show that the model can capture the levels of selectivity reported in neuroscience. Accordingly, single-cell data do not rule out localist coding schemes. We propose that the term grandmother cell should be reserved for the hypothesis that the brain implements localist representations: neurons that represent one and only one thing but respond to multiple things.
Original languageEnglish
Pages (from-to)1-33
Number of pages33
JournalLanguage, Cognition and Neuroscience
Volume32
Issue number3
Early online date29 Nov 2016
DOIs
Publication statusPublished - 2017

Structured keywords

  • Language
  • Cognitive Science
  • Physical and Mental Health
  • Mental Health Data Science

Keywords

  • Localist coding
  • Grandmother cells
  • Selectivity
  • Computational models
  • Visual word recognition

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