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A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas (2013)
BibTeX
@MISC{Mender13amodel,
author = {Bedeho M. W. Mender and Simon M. Stringer},
title = {A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas},
year = {2013}
}
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Abstract
We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.
Keyphrases
primate parietal area self-organizing head-centered visual response core model component head-centered output neuron output neuron synaptic learning rule head-centered visual representation eye position gain input pattern neural network model synaptic connection retinal receptive field visually-guided learning competitive output layer afferent synaptic connection successful self-organization associative trace learning rule network architecture input neuron
