Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants |
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Authors: | D M Walters S M Stringer |
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Institution: | (1) School of Information Technology and Electrical Engineering, The University of Queensland, Queensland, Brisbane, Australia;(2) Queensland Brain Institute, The University of Queensland, Queensland, Brisbane, Australia |
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Abstract: | A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may
self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of
location within an environment. It is of vital importance that this internal representation of position is updated at the
correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically
plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to
effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises
a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn
two different speeds of rotation when implemented with two different values for the time constant, and without the need to
alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and
path integration of spatial view. |
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Keywords: | |
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