Microscopic probabilistic information processing inferred from a macroscopic bias in sensory-motor interaction

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K12165
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 61040:Soft computing-related
• Research Institution: Ritsumeikan University
• Principal Investigator: Tsubo Yasuhiro 立命館大学, 情報理工学部, 准教授 (40384721)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 脳・神経 / 神経科学 / 情報工学 / 生体生命情報学 / 事象関連電位 / 確率型情報処理 / 低エネルギー / バイアス

Outline of Final Research Achievements

For realization of a sustainable society, it is necessary to elucidate new computational principles that mimic the brain, which is a representative energy-efficient information-processing device. In this research project, we focused on the bias of timing in tapping motion to periodic sensory stimuli, which is one of the typical differences between human information processing and machine learning, and investigated the requirements for constructing a stochastic neural circuit model that can reproduce the bias by measuring the relationship between statistical properties of the bias and brain activity as event-related potentials, and by examining the neural circuit structure in the relevant brain regions. We considered a new mathematical model that does not use optimization as a steppingstone to stochastic information processing.

Free Research Field

神経情報科学

Academic Significance and Societal Importance of the Research Achievements

これまで脳を模倣した新しい計算原理の探索が様々なアプローチで行われてきたが，脳の低エネルギー性を生かすような確率型情報処理様式の解明には至らなかった．本研究課題では，確率型情報処理の足がかりとなる，最適化を用いない新しい数理モデルを考察し，その数理モデルの改良の評価方法としてのタッピング誤差分布，内部状態分布を与えたことで，今後の脳型確率情報処理様式の解明に向けた研究の前進に貢献した．