Bayesian representation learning in the cortex regulated by acetylcholine

Junichiro Hirayama, Junichiro Yoshimoto, Shin Ishii

研究成果: Article査読

12 被引用数 (Scopus)

抄録

A brain needs to detect an environmental change and to quickly learn internal representations necessary in a new environment. This paper presents a theoretical model of cortical representation learning that can adapt to dynamic environments, incorporating the results by previous studies on the functional role of acetylcholine (ACh). We adopt the probabilistic principal component analysis (PPCA) as a functional model of cortical representation learning, and present an on-line learning method for PPCA according to Bayesian inference, including a heuristic criterion for model selection. Our approach is examined in two types of simulations with synthesized and realistic data sets, in which our model is able to re-learn new representation bases after the environment changes. Our model implies the possibility that a higher-level recognition regulates the cortical ACh release in the lower-level, and that the ACh level alters the learning dynamics of a local circuit in order to continuously acquire appropriate representations in a dynamic environment.

本文言語English
ページ(範囲)1391-1400
ページ数10
ジャーナルNeural Networks
17
10
DOI
出版ステータスPublished - 12-2004
外部発表はい

All Science Journal Classification (ASJC) codes

  • 認知神経科学
  • 人工知能

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