Mean Field Theory of a Neural Network.
Abstract
A single cell theory for the development of selectivity and ocular dominance in visual cortex has been generalized to incorporate more realistic neural networks that approximate the actual anatomy of small regions of cortex. In particular we have analyzed a network consisting of excitatory and inhibitory cells, both of which may receive information from LGN and then interact through cortico-cortical synapses in a mean field approximation. Our investigation of the evolution of a cell in this mean field network indicates that many of the results on existence and stability of fixed points that have been obtained previously in the single cell theory can be successfully generalized here. We can, in addition, make explicit further statements concerning the independent effects of excitatory and inhibitory neurons on selectivity and ocular dominance. For example, shutting off inhibitory cells may be selective but there is no theoretical necessity that they be so. Further the intracortical inhibitory synapses do not have to be very responsive to visual experience. Most of the learning process can occur among the excitatory LGN- cortical synapses. Some of these ideas are compared with experimental results.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 14, 1988
- Accession Number
- ADA190801
Entities
People
- Christopher L. Scofield
- Leon Cooper
Organizations
- Brown University