Abstract
Because episodic memory includes a time series of events, an underlying dynamics for the formation of episodic memory is considered to employ a mechanism of encoding sequences of events. The “Cantor coding” hypothesis in hippocampal CA1 has been proposed, which provides a scheme for encoding temporal sequences of events. Here, in order for investigating the Cantor coding in detail, we constructed a model for the CA1 network which consists of conductance-based model neurons. It was assumed that the CA3 outputs temporal sequences of spatial patterns to CA1. It was shown that the output patterns of CA1 were hierarchically clustered in a self-similar manner according to the similarity of input time series. The dependency of the efficacy of encoding on the input time interval and its robustness against noise was investigated.
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Acknowledgments
This work was supported in part by Grants-in-Aid (No. 18019002 and 18047001) for Scientific Research on Priority Areas from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the twenty-first century COE Program of the Department of Mathematics of Hokkaido University.
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Yamaguti, Y., Kuroda, S., Tsuda, I. (2011). Representation of Time-Series by a Self-Similar Set in a Model of Hippocampal CA1. In: Wang, R., Gu, F. (eds) Advances in Cognitive Neurodynamics (II). Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9695-1_14
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DOI: https://doi.org/10.1007/978-90-481-9695-1_14
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