Neural implementation of predictive coding

2023 Fiscal Year Final Research Report

• Project Area: Frontiers in brain and life sciences on active information gain in an uncertain environment
• Project/Area Number: 21H05168
• Research Category: Grant-in-Aid for Transformative Research Areas (B)
• Allocation Type: Single-year Grants
• Review Section: Transformative Research Areas, Section (IV)
• Research Institution: Nagoya University
• Principal Investigator: Osakada Fumitaka 名古屋大学, 創薬科学研究科, 准教授 (60455334)
• Project Period (FY): 2021-08-23 – 2024-03-31
• Keywords: 予測符号化 / 自由エネルギー原理 / 予測誤差 / 神経回路 / 階層性 / イメージング / 限定合理性 / 数理モデル

Outline of Final Research Achievements

Animals, including humans, actively and flexibly adapt to the external environment by estimating environmental conditions based on sensory information and past experience. The brain constructs an internal model and generates perception and cognition by comparing the predictions generated by the internal model with the actual sensory inputs. This idea has been formulated in predictive coding theory, but how it is implemented in the brain is unknown. In this study, we aimed to investigate the neural implementation of predictive coding theory. We revealed the hierarchical neural circuit structure responsible for predictive coding by using VR technology combined with viral vectors, optical imaging, electrophysiological recordings, optogenetics, and mathematical modeling.

Free Research Field

神経科学

Academic Significance and Societal Importance of the Research Achievements

本研究により、予測符号化の計算理論、アルゴリズム、脳内実装が明らかになった。予測符号化は知覚や感覚運動連関の理解のみならず、神経疾患や精神疾患を予測と観察のバランス異常として捉えることができる。本研究により、神経疾患や精神疾患の診断法や新たな治療戦略の創出に貢献できると期待される。