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2002 Fiscal Year Final Research Report Summary

Theoretical modes for learning and memory in the microbrain system

Research Project

Project/Area Number 11168224
Research Category

Grant-in-Aid for Scientific Research on Priority Areas (A)

Allocation TypeSingle-year Grants
Review Section Biological Sciences
Research InstitutionKyushu Institute of Technology

Principal Investigator

MATSUOKA Kiyotoshi  Graduate School of Life Science and Systems Engineering, Professor, 大学院・生命体工学研究科, 教授 (90110840)

Project Period (FY) 1999 – 2001
Keywordslearning / memory / Hebbian learning / anti-Hebbian learning
Research Abstract

The purpose of this study was to investigate learning / memory mechanisms in lower animals by means of mathematical models. In particular we have focused on a Hebb-type learning.
A typical model for Hebbian / anti-Hebbian learning is as follows : when the pre- and post-synaptic activations occur at the same time, the relevant synaptic weight increases / decreases. According to recent neuro-physiological findings, however, the change in the synaptic weight depends on the timing between pre- and post-synaptic activities. Namely, if the pre-synaptic activation precedes the post-synaptic activation, then LTP is induced. On the other hand, if the order is reversed, then LTP appears. This kind of asymmetric feature in synaptic plasticity must play a very important role in the learning of temporal patterns in animals.
In this study we first built a basic mathematical model for temporally asymmetric Hebbian learning. Using the basic model, we devised more elaborated models that would explain some neuronal phenomena, for example, (1) oscillatory behavior of a neural circuit, (2) neural integrators, (3) neural memory for certain periodic stimuli as seen in the visual system of the crayfish. Through the modeling, we were able to clarify some functional mechanisms of the temporally Hebbian learning rule.

  • Research Products

    (4 results)

All Other

All Publications (4 results)

  • [Publications] Matsuoka, K.: "A general theory of a class of linear neural nets for principal and minor component analysis"Artificial Life and Robotics. Vol.3. 246-254 (1999)

    • Description
      「研究成果報告書概要(和文)」より
  • [Publications] Matsuoka, K.: "A model analysis of temporally asymmetric Hebbian learning"Recent Advances in Simulation, Computational Methods and Soft Computing. 193-198 (2002)

    • Description
      「研究成果報告書概要(和文)」より
  • [Publications] Matsuoka, K.: "A general theory of a class of linear neural nets for principal and minor component analysis"Artificial Life and Robotics. Vol. 3. 246-254 (1999)

    • Description
      「研究成果報告書概要(欧文)」より
  • [Publications] Matsuoka, K.: "A model analysis of temporally asymmetric Hebbian learning"Recent Advances in Simulation Computational Methods and Soft Computing. 193-198 (2002)

    • Description
      「研究成果報告書概要(欧文)」より

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Published: 2004-04-14  

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