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  • Cited by 1
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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Földiák, Peter 2013. Principles of Neural Coding.


Localist representation can improve efficiency for detection and counting

  • Horace Barlow (a1) and Anthony Gardner-Medwin (a2)
  • DOI:
  • Published online: 01 August 2000

Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility.

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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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