Robust path integration in the entorhinal grid cell system with hippocampal feed-back |
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Authors: | Dávid Samu Péter Er?s Balázs Ujfalussy Tamás Kiss |
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Institution: | (1) Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics, Hungarian Academy of Sciences, Konkoly-Thegeút 29 – 33, 1121 Budapest, Hungary |
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Abstract: | Animals are able to update their knowledge about their current position solely by integrating the speed and the direction
of their movement, which is known as path integration. Recent discoveries suggest that grid cells in the medial entorhinal
cortex might perform some of the essential underlying computations of path integration. However, a major concern over path
integration is that as the measurement of speed and direction is inaccurate, the representation of the position will become
increasingly unreliable. In this paper, we study how allothetic inputs can be used to continually correct the accumulating
error in the path integrator system. We set up the model of a mobile agent equipped with the entorhinal representation of
idiothetic (grid cell) and allothetic (visual cells) information and simulated its place learning in a virtual environment.
Due to competitive learning, a robust hippocampal place code emerges rapidly in the model. At the same time, the hippocampo-entorhinal
feed-back connections are modified via Hebbian learning in order to allow hippocampal place cells to influence the attractor
dynamics in the entorhinal cortex. We show that the continuous feed-back from the integrated hippocampal place representation
is able to stabilize the grid cell code.
This research was supported by the EU Framework 6 ICEA project (IST-4-027819-IP). |
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Keywords: | Sensor fusion Place representation Learning Noise Error correction |
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