A Chaotic Model of Hippocampus-Neocortex

• Published in: Advances in Natural Computation pp. 439 - 448
• Main Authors: Kuremoto, Takashi, Eto, Tsuyoshi, Kobayashi, Kunikazu, Obayashi, Masanao
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture 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
• ISBN: 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539087_56

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.