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Population Code Dynamics in Categorical Perception.

Chihiro I Tajima1, Satohiro Tajima2, Kowa Koida3

  • 1Graduate School of Information Science and Technology, the University of Tokyo. 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.

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Summary
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This study introduces a recurrent neural network model to explain categorical perception, demonstrating how neural dynamics approximate optimal inference for sensory processing and memory.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Neuroscience

Background:

  • Categorical perception significantly influences stimulus recognition but lacks a unified explanation for its complex interactions with sensory processing.
  • Existing models struggle to consistently explain diverse neurophysiological and cognitive phenomena related to categorical perception.

Purpose of the Study:

  • To propose a unified recurrent neural network (RNN) model for processing categorical information in stimuli.
  • To demonstrate how this model can account for various aspects of categorical perception, including task-dependent neural modulation and memory effects.
  • To validate the model's predictions through empirical investigation in the monkey visual cortex.

Main Methods:

  • Development of a recurrent neural network model approximating hierarchical Bayesian estimation for stimulus processing.
  • Analysis of neurophysiological and cognitive phenomena, including neural response modulation, population representation clustering, perceptual memory, and discrimination thresholds.
  • Experimental validation using neural population dynamics in the monkey visual cortex during color categorization and discrimination tasks.

Main Results:

  • The proposed RNN model successfully explains diverse phenomena associated with categorical perception within a consistent framework.
  • Empirical data from monkey visual cortex revealed temporally-evolving biases in neuronal population representations toward focal colors during categorization tasks.
  • These findings support the model's hypothesis that categorical perception arises from recurrent neural dynamics approximating probabilistic inference.

Conclusions:

  • Recurrent neural dynamics can achieve categorical perception by approximating optimal probabilistic inference in dynamic environments.
  • The proposed model offers a unified framework for understanding the complex interplay between categorical perception and other sensory processing aspects.
  • The study provides neurophysiological evidence supporting the computational model in a primate visual system.