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Population Code Dynamics in Categorical Perception.

  • Tajima CI Graduate School of Information Science and Technology, the University of Tokyo. 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
  • Tajima S Department of Basic Neuroscience, University of Geneva. CMU, 1 rue Michel Servet, 1211 Genève, Switzerland.
  • Koida K EIIRIS, Toyohashi University of Technology. 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi, 441-8580, Japan.
  • Komatsu H National Institute for Physiological Sciences. 38 Nishigonaka Myodaiji, Okazaki, Aichi, 444-8585, Japan.
  • Aihara K Graduate School of Information Science and Technology, the University of Tokyo. 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
  • Suzuki H Graduate School of Information Science and Technology, the University of Tokyo. 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
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  • 2016-03-04
Published in:
  • Scientific reports. - 2016
Animals
Humans
Macaca mulatta
Models, Neurological
Nerve Net
Visual Cortex
Visual Perception
English Categorical perception is a ubiquitous function in sensory information processing, and is reported to have important influences on the recognition of presented and/or memorized stimuli. However, such complex interactions among categorical perception and other aspects of sensory processing have not been explained well in a unified manner. Here, we propose a recurrent neural network model to process categorical information of stimuli, which approximately realizes a hierarchical Bayesian estimation on stimuli. The model accounts for a wide variety of neurophysiological and cognitive phenomena in a consistent framework. In particular, the reported complexity of categorical effects, including (i) task-dependent modulation of neural response, (ii) clustering of neural population representation, (iii) temporal evolution of perceptual color memory, and (iv) a non-uniform discrimination threshold, are explained as different aspects of a single model. Moreover, we directly examine key model behaviors in the monkey visual cortex by analyzing neural population dynamics during categorization and discrimination of color stimuli. We find that the categorical task causes temporally-evolving biases in the neuronal population representations toward the focal colors, which supports the proposed model. These results suggest that categorical perception can be achieved by recurrent neural dynamics that approximates optimal probabilistic inference in the changing environment.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/222152
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