A Chaotic Model of Hippocampus-Neocortex

To realize mutual association function, we propose a hippoca- mpus-neocortex model with multi-layered chaotic neural network (MCNN). The model is based on Ito etal.’s hippocampus-cortex model (2000), which is able to recall temporal patterns, and form long-term memory. The MCNN consists of plural ch...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 439 - 448
Main Authors Kuremoto, Takashi, Eto, Tsuyoshi, Kobayashi, Kunikazu, Obayashi, Masanao
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Association Cortex
Chaotic Neural Network
Computer science; control theory; systems
Connection Weight
Exact sciences and technology
Input Pattern
Mutual Association
Chaos
Itô equation
Computer simulation
Time series
Neural network
Long term
Modeling
Multilayer network
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Summary:To realize mutual association function, we propose a hippoca- mpus-neocortex model with multi-layered chaotic neural network (MCNN). The model is based on Ito etal.’s hippocampus-cortex model (2000), which is able to recall temporal patterns, and form long-term memory. The MCNN consists of plural chaotic neural networks (CNNs), whose each CNN layer is a classical association model proposed by Aihara. MCNN realizes mutual association using incremental and relational learning between layers, and it is introduced into CA3 of hippocampus. This chaotic hippocampus-neocortex model intends to retrieve relative multiple time series patterns which are stored (experienced) before when one common pattern is represented. Computer simulations verified the efficiency of proposed model.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_56