Excitation-inhibition equilibrium and information processing function guided by synaptic learning

2022 Fiscal Year Final Research Report

• Project/Area Number: 18K11486
• 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: Shibaura Institute of Technology (2022); Fukuoka University (2018-2021)
• Principal Investigator: Hosaka Ryosuke 芝浦工業大学, システム理工学部, 准教授 (80569210)
• Project Period (FY): 2018-04-01 – 2023-03-31
• Keywords: 興奮性 / 抑制性

Outline of Final Research Achievements

Excitatory and inhibitory neurons in a neural circuit are balanced in number and strength (excitatory-inhibitory balance) and provide many neural functions. In systems that process non-stationary inputs, the balance state should be organized dynamically by synaptic learning, but the optimal learning function for this purpose is unknown. Moreover, is the optimal learning function for the balance the same as the optimal learning function for information processing? In this study, we answer these questions by deriving the optimal synaptic learning function in terms of balance and its information processing in an interconnected neural circuit with STDP learning rules for excitatory and inhibitory synapses.

Free Research Field

計算論的神経科学

Academic Significance and Societal Importance of the Research Achievements

本研究でターゲットとした抑制性シナプスのSTDP学習は実験的にも発見されたばかりであり、抑制性シナプスにSTDP学習を持つ神経ネットワークの機能解明はいまだ不十分であった。興奮性シナプスにSTDP学習を持つ神経ネットワークで見られた欠点が、本研究の抑制性シナプスのSTDP学習の導入によって克服されれば、抑制性シナプスのSTDP学習の新たな機能を発見したこととなり、当該研究分野に大きなインパクトを与えられると考えられた。この目的はある程度達成された。