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This article is cited in 2 scientific papers (total in 2 papers)
Intellectual Systems and Technologies
Chaotic models of the hippocampus for dynamic pattern recognition
E. N. Benderskaya, A. O. Pereshein Peter the Great Saint-Petersburg Polytechnic University
Abstract:
The paper carried out an analysis of using systems with chaotic dynamics to solve the problem of dynamic pattern recognition. We reviewed the existing chaotic models of the hippocampus for storage, coding and retrieval of dynamic information. An episodic chaotic associative memory models proposed by Y. Osana, a hippocampus-neocortex model proposed by T. Kuremoto, and Tsuda's hippocampus model are considered in detail. The first two of these models incorporate Aihara's chaotic neural networks. We compared the selected models based on the simulation results. It is shown that chaotic dynamics is necessary in order to take into account the context of dynamic pattern recognition problems. Trends of further modification of the models are also proposed.
Keywords:
artificial intelligence, nonlinear dynamics, chaotic neural networks, episodic memory, models of the hippocampus, dynamic pattern recognition.
Citation:
E. N. Benderskaya, A. O. Pereshein, “Chaotic models of the hippocampus for dynamic pattern recognition”, St. Petersburg Polytechnical University Journal. Computer Science. Telecommunication and Control Sys, 2015, no. 6(234), 56–69
Linking options:
https://www.mathnet.ru/eng/ntitu136 https://www.mathnet.ru/eng/ntitu/y2015/i6/p56
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Abstract page: | 271 | Full-text PDF : | 143 |
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