Transformation Learning of Neural Representation from Population Codes to Firing Rate Codes in Smooth Pursuit Eye Movements

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概要

• 論文の詳細を見る

• It has been shown that environmental information is represented as population codes in cortical sensory areas. How does the brain use these population codes? In this paper, we examine this problem using the smooth pursuit eye movements (pursuit). During pursuit, a retinal slip signal and an extraretinal signal are represented as population codes in the higher cortical visual areas, MT and MST. On the other band, motor commands for the eye movements are represented as firing rate codes in the Purkinje cells of cerebellum. We propose that the acquisition of transformation of these neural representations is achieved through synaptic plasticity in the cerebellar cortex, and construct the model based on the feedback-error-learning. Our scheme successfully demonstrates the neural code change by means of well-known cerebellar curcuit and the learning rule based on physiological experiments.

• 社団法人電子情報通信学会の論文
• 2002-03-01

著者

• KAWATO Mitsuo

  Nara Institute of Science and Technology

• SHIBATA Tomohiro

  Metalearing and Neuromodulation, CREST, Japan Science and Technology Corporation

• TABATA Hiromitsu

  Graduate School of Medicine, Kyoto University

• KAWATO Mitsuo

  ATR Computational Neuroscience Labs

• Tabata Hiromitsu

  Nara Institute Of Science And Technology:national Institute Of Advanced Industrial Science And Techn

• Kawato Mitsuo

  Nara Institute Of Science And Technology:atr Human Information Science Laboratories

• Shibata Tomohiro

  Nara Institute Of Science And Technology:atr Human Information Science Laboratories

• TAGUCHI Shinya

  Nara Institute of Science and Technology

• SHIBATA Tomohiro

  Creating the Brain, CREST, Japan Science and Technology Corporation

• Taguchi Shinya

  Nara Institute Of Science And Technology:atr Human Information Science Laboratories

関連論文

• Anterior and superior lateral occipito-temporal cortex responsible for target motion prediction during overt and covert visual pursuit
• A model of smooth pursuit in primates based on learning the target dynamics
• Reinforcement learning with via-point representation
• A Novel Smooth Pursuit Model Based on Recurrent Neural Connections in the MST Cell Population
• Transformation Learning of Neural Representation from Population Codes to Firing Rate Codes in Smooth Pursuit Eye Movements

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