Autoregressive model of the hippocampal representation of events

A. Lőrincz, Gábor Szirtes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The hippocampal formation is believed to play a central role in forming long lasting representation of events. However, in contrast to the continuous nature of sensory signal flow, events are spatially and temporally bounded processes. In this paper we are interested in the kind of representation that allows for detecting and/or predicting events. Based on new results on the identification problem of linear hidden processes, we propose a general signal encoding model that can represent causal relationships used to define events. We translate the model into a connectionist structure in which parameter learning follows biologically plausible rules. We also speculate on the resemblance of the resulting structure to the connection system of the hippocampal formation. When our signal encoding model is applied on spatially anchored inputs, its different parts feature spatially localized and periodic neural activity similar to those found in the hippocampus and in the entorhinal cortex, respectively. These emergent forms of spatial activity differentiates our model from other computational models of (spatial) memory as the model has not been explicitly designed to deal with spatial information. We speculate that our model may describe the core function of the hippocampal region in forming episodic memory and supporting spatial navigation.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages1885-1892
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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Lőrincz, A., & Szirtes, G. (2009). Autoregressive model of the hippocampal representation of events. In Proceedings of the International Joint Conference on Neural Networks (pp. 1885-1892). [5178796] https://doi.org/10.1109/IJCNN.2009.5178796

Autoregressive model of the hippocampal representation of events. / Lőrincz, A.; Szirtes, Gábor.

Proceedings of the International Joint Conference on Neural Networks. 2009. p. 1885-1892 5178796.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lőrincz, A & Szirtes, G 2009, Autoregressive model of the hippocampal representation of events. in Proceedings of the International Joint Conference on Neural Networks., 5178796, pp. 1885-1892, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 6/14/09. https://doi.org/10.1109/IJCNN.2009.5178796
Lőrincz A, Szirtes G. Autoregressive model of the hippocampal representation of events. In Proceedings of the International Joint Conference on Neural Networks. 2009. p. 1885-1892. 5178796 https://doi.org/10.1109/IJCNN.2009.5178796
Lőrincz, A. ; Szirtes, Gábor. / Autoregressive model of the hippocampal representation of events. Proceedings of the International Joint Conference on Neural Networks. 2009. pp. 1885-1892
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