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Self-Organization of Orientation Maps, Lateral Connections, and
Dynamic Receptive Fields in the Primary Visual Cortex
Joseph
Sirosh
,
Risto Miikkulainen,
and
James A. Bednar
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
sirosh,risto,jbednar@cs.utexas.edu
Abstract
Through massively parallel computational simulations, we studied how
a large network of simple neural elements (the RF-LISSOM model) could
develop a functional organization similar to that of the primary visual
cortex. It was found that starting from a 'tabula rasa' state, the
afferent and lateral connections in the network self-organized
cooperatively and simultaneously through a common Hebbian mechanism, and
produced receptive fields (RFs), orientation maps, and patterns of
lateral connections that follow the receptive field organization. Second,
we hypothesized that similar self-organizing mechanisms continue
operating in the adult cortex, maintaining it in a continuously-adapting
dynamic equilibrium with the input, and tested this hypothesis on the
self-organized model. When the equilibrium was perturbed by a retinal
scotoma, RFs expanded in size in a reversible fashion, matching recent
neurobiological observations in the cat and psychophysical experiments
in the human. Third, a possible functional role for the lateral
connections in the cortex was verified in the model. The lateral
connections learned correlations in the network activity, and in
processing retinal input, filtered out redundancies and established a
sparse coding of the input. The conclusion is that the lateral
connections in the cortex could act as as a negative filter that allows
the cortex to efficiently process the massive amounts of visual
information presented by the environment.
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