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 language | English |
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Pages (from-to) | 257-273 |
Number of pages | 17 |
Journal | Language, Cognition and Neuroscience |
Volume | 32 |
Issue number | 3 |
Early online date | 27 Jan 2017 |
DOIs | |
Publication status | Published - 16 Mar 2017 |
Research Groups and Themes
- Language
- Cognitive Science
Keywords
- grandmother cells
- localist representations
- distributed representationss
- neural networks
- deep networks