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2023 Fiscal Year Final Research Report

A Comprehensive Theoretical Survey on Functionalities and Properties of Adult Neurogenesis under the Influence of Slow Oscillations

Research Project

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Project/Area Number 19K16885
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 51010:Basic brain sciences-related
Research InstitutionOkinawa Institute of Science and Technology Graduate University

Principal Investigator

FUNG CHI CHUNG  沖縄科学技術大学院大学, 神経情報・脳計算ユニット, 客員研究員 (80757203)

Project Period (FY) 2019-04-01 – 2024-03-31
KeywordsHebbian Learning / Machine Learning / Adult Neurogenesis / Pattern Separation / Dentate Gyrus
Outline of Final Research Achievements

In this work, we found that synaptic competition improves the discrimination of overlapping memory patterns by reducing memory interference through competition between newly generated neurons and mature neurons. Neural networks trained with synaptic competition-based learning rules show superior performance, especially with fewer training samples, compared to those trained with backpropagation algorithms. Computational models indicate that synaptic competition plays a crucial role in information processing. Behavioral experiments in the literature confirm that mice with suppressed adult neurogenesis have difficulty discriminating interfering memories, supporting the importance of synaptic competition and neurogenesis.

Free Research Field

Computational Neuroscience

Academic Significance and Societal Importance of the Research Achievements

本研究は、シナプス競争が記憶パターンの識別を改善し、成人期の神経新生が記憶の干渉を減少させるメカニズムを解明しました。シナプス競争に基づく学習ルールが少数の訓練サンプルで優れた性能を示し、情報処理における重要性を示す成果です。または、この研究は、アルツハイマー病などの記憶障害の新たな治療法の開発に寄与し、シナプス競争に基づく学習アルゴリズムはAIや機械学習の技術革新を促進する可能性があります。

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Published: 2025-01-30  

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