Dynamical Neural Network Model for Hippocampal Memory

Osamu ARAKI  Kazuyuki AIHARA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E81-A   No.9   pp.1824-1832
Publication Date: 1998/09/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)
Category: Neural Networks
neural networks,  hippocampus,  self-organization,  LTP,  LTD,  propagation of LTD,  

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The hippocampus is thought to play an important role in the transformation from short-term memory into long-term memory, which is called consolidation. The physiological phenomenon of synaptic change called LTP or LTD has been studied as a basic mechanism for learning and memory. The neural network mechanism of the consolidation, however, is not clarified yet. The authors' approach is to construct information processing theory in learning and memory, which can explain the physiological data and behavioral data. This paper proposes a dynamical hippocampal model which can store and recall spatial input patterns. The authors assume that the primary functions of hippocampus are to store episodic information of sensory signals and to keep them for a while until the neocortex stores them as a long-term memory. On the basis of the hippocampal architecture and hypothetical synaptic dynamics of LTP/LTD, the authors construct a hippocampal model. This model considers: (1) divergent connections, (2) the synaptic dynamics of LTP and LTD based on pre- and postsynaptic coincidence, and (3) propagation of LTD. Computer simulations show that this model can store and recall its input spatial pattern by self-organizing closed activating pathways. By the backward propagation of LTD, the synaptic pathway for a specific spatial input pattern can be selected among the divergent closed connections. In addition, the output pattern also suggests that this model is sensitive to the temporal timing of input signals. This timing sensitivity suggests the applicability to spatio-temporal input patterns of this model. Future extensions of this model are also discussed.