Journal of Computational Neuroscience

, Volume 25, Issue 1, pp 169–187 | Cite as

Neural coding of categories: information efficiency and optimal population codes

  • Laurent Bonnasse-GahotEmail author
  • Jean-Pierre Nadal


This paper deals with the analytical study of coding a discrete set of categories by a large assembly of neurons. We consider population coding schemes, which can also be seen as instances of exemplar models proposed in the literature to account for phenomena in the psychophysics of categorization. We quantify the coding efficiency by the mutual information between the set of categories and the neural code, and we characterize the properties of the most efficient codes, considering different regimes corresponding essentially to different signal-to-noise ratio. One main outcome is to find that, in a high signal-to-noise ratio limit, the Fisher information at the population level should be the greatest between categories, which is achieved by having many cells with the stimulus-discriminating parts (steepest slope) of their tuning curves placed in the transition regions between categories in stimulus space. We show that these properties are in good agreement with both psychophysical data and with the neurophysiology of the inferotemporal cortex in the monkey, a cortex area known to be specifically involved in classification tasks.


Categorization Population coding Exemplar models Mutual information Inferotemporal cortex 



This work is part of a project “Acqlang” supported by the French National Research Agency (ANR-05-BLAN-0065-01). LBG acknowledges a fellowship from the Délégation Générale pour l’Armement. JPN is a Centre National de la Recherche Scientifique member. The initial motivation for this work comes from (psycho- and neuro-) computational issues in the perception of phonemes: we thank Sharon Peperkamp and Janet Pierrehumbert for introducing us to this topic and for valuable discussions. LBG is grateful to the members of the Laboratoire de Sciences Cognitives et Psycholinguistique de l’ENS, especially to Emmanuel Dupoux, for numerous and stimulating discussions. We acknowledge useful inputs from the referees, and most especially, we thank one of them for a detailed list of constructive comments.


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© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Centre d’Analyse et de Mathématique Sociales (CAMS, UMR 8557 CNRS-EHESS)Ecole des Hautes Etudes en Sciences SocialesParis Cedex 06France
  2. 2.Laboratoire de Physique Statistique (LPS, UMR 8550 CNRS-ENS-Paris 6-Paris 7)Ecole Normale SupérieureParis Cedex 05France

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