Bayesian representation learning in the cortex regulated by acetylcholine.
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.
Hirayama
Junichiro
J
Nara Institute of Science and Technology 8916-5 Takayama, Ikoma, Nara 630-0192, Japan. junich-h@is.naist.jp
Yoshimoto
Junichiro
J
Ishii
Shin
S
D000109:Acetylcholine / Q000502:physiology*; D000818:Animals; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000502:physiology
D000109:Acetylcholine