Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Milner, B., Corkin, S., Teuber, H.L.: Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H. M. Neuropsychologia 6, 317–338 (1968)
Hampson, R.E., Simeral, J.D., Deadwyler, S.A.: Distribution of spatial and nonspatial information in dorsal hippocampus. Nature 402, 610–614 (1999)
Fortin, N.J., Wright, S.P., Eichenbaum, H.: Recollection-like memory retrieval in rats dependent on the hippocampus. Nature 431, 188–191 (2004)
Remondes, M., Schuman, E.M.: Role for a cortical input to hippocampal area CA1 in the consolidation of a long-term memory. Nature 431, 699–703 (2004)
Ito, M., Kuroiwa, J., Miyake, S.: A model of hippocampus-neocortex for episodic memory. In: The 5th Inter. Conf. on Neural Inf. Process, vol. 1P-16, pp. 431–434 (1998)
Ito, M., Kuroiwa, J., Miyake, S.: A neural network model of memory systems using hippocampus. The Tran. of the IEICE J82-D-II, 276–286 (1999) (in Japanese)
Ito, M., Miyake, S., Inawashiro, S., Kuroiwa, J., Sawada, Y.: Long-term memory of temporal patterns in a hippocampus-cortex model. Technical Report of IEICE NLP2000-18, 25–32 (2000) (in Japanese)
Freeman, W.J.: Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Bio. Cybern. 56, 139–150 (1987)
Skarda, C.A., Freeman, W.J.: How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10, 161–195 (1987)
Aihara, K.: Chaotic neural networks. Phys. Lett. A 144, 333–340 (1990)
Adachi, M., Aihara, K.: Associative dynamics in chaotic neural network. Neural Networks 10, 83–98 (1997)
Obayashi, M., Watanabe, K., Kobayashi, K.: Chaotic neural networks with Radial Basis Functions and its application to memory search problem. Trans. IEE of Japan 120-C, 1441–1446 (2000) (in Japanese)
Obayashi, M., Yuda, K., Omiya, R., Kobayashi, K.: Associative memory and mutual information in a chaotic neural network introducing function typed synaptic weights. IEEJ Trans. EIS 123, 1631–1637 (2003) (in Japanese)
Lee, R.S.T.: A transient-chaotic autoassociative network (TCAN) based on Lee Oscillators. IEEE Trans. on Neural Networks 15, 1228–1243 (2004)
Wang, L.P., Li, S., Tian, F.Y., Fu, X.J.: A Noisy Chaotic Neural Network for Solving Combinatorial Optimization Problems: Stochastic Chaotic Simulated Annealing. IEEE Trans. on Sys., Man, Cybern., Part B - Cybern. 34, 2119–2125 (2004)
Kuremoto, T., Eto, T., Obayashi, M., Kobayashi, K.: A Multi-layered Chaotic Neural Network for Associative Memory. In: Proc. of SICE Annual Conference 2005, Okayama (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuremoto, T., Eto, T., Kobayashi, K., Obayashi, M. (2005). A Chaotic Model of Hippocampus-Neocortex. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_56
Download citation
DOI: https://doi.org/10.1007/11539087_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)