Presentation 2004/3/12
Cortical representation learning regulated by acetylcholine
Junichiro HIRAYAMA, Junichiro YOSHIMOTO, Shin ISHII,
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Abstract(in English) A brain needs to detect an environmental change and to quickly learn internal representations necessary in a new environment. This report presents a theoretical model of cortical representational learning that can adapt to dynamic environments, incorporating the results by previous studies on a functional role of acetylcholine (ACh). We adopt the probabilistic principal component analysis (PPCA) as a functional model of cortical representational learning, and present an on-line learning method for PPCA according to Bayesian inference. Our approach is examined in two types of simulations with synthesized and realistic datasets, in which our model is able to re-learn new representation bases after environmental changes. We suggest that the function of ACh corresponds to the learning rate in our learning model and describe both biological and computational studies related to our model.
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Keyword(in English) neuromodulation / acetylcholine / learning rate / internal representation / on-line variational Bayes method
Paper # NC2003-210
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Committee NC
Conference Date 2004/3/12(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Cortical representation learning regulated by acetylcholine
Sub Title (in English)
Keyword(1) neuromodulation
Keyword(2) acetylcholine
Keyword(3) learning rate
Keyword(4) internal representation
Keyword(5) on-line variational Bayes method
1st Author's Name Junichiro HIRAYAMA
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Junichiro YOSHIMOTO
2nd Author's Affiliation CREST, Japan Science and Technology Agency
3rd Author's Name Shin ISHII
3rd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
Date 2004/3/12
Paper # NC2003-210
Volume (vol) vol.103
Number (no) 734
Page pp.pp.-
#Pages 6
Date of Issue