Computational Study on the Neural Mechanism of Sequential Pattern
Memory
Masahiko Morita
Abstract:
The brain stores various kinds of temporal sequences as long-term
memories, such as motor sequences, episodes, and melodies. The
present study aims at clarifying the general principle underlying such
memories. For this purpose, the memory mechanism of sequential
patterns is examined from the viewpoint of computational theory and
neural network modeling, and a neural network model of sequential
pattern memory based on a simple and reasonable principle is
presented. Specifically, spatiotemporal patterns varying gradually
with time are stably stored in a network consisting of pairs of
excitatory and inhibitory cells with recurrent connections; such a
pair can achieve nonmonotonic input-output characteristics which are
essential for smooth sequential recall. Storage is performed using a
simple learning algorithm which is based on the covariance rule and
requires only that the sequence be input several times, and retrieval
is highly tolerant to noise. It is thought that a similar principle
is used in cerebral memory systems, and the relevance of this model to
the brain is discussed. Also, possible roles of hippocampus and basal
ganglia in memorizing sequences are suggested.
Keywords:
Sequential pattern memory, Neural network model, Network dynamics,
Nonmonotonic characteristic, Local inhibition cell, Sparse coding,
Learning algorithm, Covariance rule.
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