Hybrid model for rodent spatial learning and localization

Rushi Bhatt, Karthik Balakrishnan, Vasant Honavar

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

Abstract

Recent work has explored a Kalman filter model of animal spatial learning the presence uncertainty in sensory as well as path integration estimates. This model was able to successfully account for several of the behavioral experiments reported in the animal navigation literature. This paper extends this model in some important directions. It accounts for the observed firing patterns of hippocampal neurons in visually symmetric environments that offer polarizing sensory cues. It incorporates mechanisms that allow for differential contribution from proximal and distal landmarks during localization. It also supports learning of associations between rewards and places to guide goal-directed navigation.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages27-32
Number of pages6
Volume1
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

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All Science Journal Classification (ASJC) codes

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Cite this

Bhatt, R., Balakrishnan, K., & Honavar, V. (1999). Hybrid model for rodent spatial learning and localization. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 27-32). IEEE.