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A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attractor network to the next head direction based on the incoming rotation signal. An associative synaptic modification rule with a short term memory trace enables preceding combination cell activity during training to be associated with the next position in the continuous attractor network. The network accounts for the presence of neurons found in the brain that respond to combinations of head direction and angular head rotation velocity. Analogous networks in the hippocampal system could self-organize to perform path integration of place and spatial view representations.

Original publication

DOI

10.1080/09548980601004032

Type

Journal article

Journal

Network

Publication Date

12/2006

Volume

17

Pages

419 - 445

Keywords

Action Potentials, Animals, Computer Simulation, Head Movements, Models, Neurological, Nerve Net, Neural Networks (Computer), Neurons, Space Perception, Synapses, Time Factors