Using Neural Networks to Model Conditional Multivariate Densities

Williams, Peter (1996) Using Neural Networks to Model Conditional Multivariate Densities. Neural Computation, 8 (4). pp. 843-854. ISSN 08997667

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Abstract

How do multiple feature maps that coexist in the same region of cerebral cortex align with each other? We hypothesize that such alignment is governed by temporal correlations: features in one map that are temporally correlated with those in another come to occupy the same spatial locations in cortex over time. To examine the feasibility of this hypothesis and to establish some of its detailed implications, we studied a multilayered, closed-loop computational model of primary sensorimotor cortex. A simulated arm moving in three dimensions formed the external environment for the model cortical regions. Coexisting proprioceptive and motor maps formed and generally aligned in a fashion consistent with the temporal correlation hypothesis. For example, in simulated proprioceptive sensory cortex the map of elements responding strongly to stretch of a particular muscle matched the map of tension sensitivity in antagonist muscles. In simulated primary motor cortex the map of elements responding strongly to increased tension in specific muscles matched the map of output elements for the same muscles. These computational results suggest specific experimental measurements that can support or refute the temporal correlation hypothesis for map alignments.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: EPrints Services
Date Deposited: 06 Feb 2012 20:41
Last Modified: 13 Jun 2012 13:25
URI: http://sro.sussex.ac.uk/id/eprint/27478
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