Cortical map reorganization as a competitive process

Authors:
Granger G. Sutton, III;James A. Reggia;Steven L. Armentrout;C. Lynne D'Autrechy
Affiliations:
-;-;-;-
Venue:
Neural Computation
Year:
1994

Citing 0
Cited 6

Temporally asymmetric learning supports sequence processing in multi-winner self-organizing maps

Neural Computation
Mirror Symmetric Topographic Maps Can Arise from Activity-Dependent Synaptic Changes

Neural Computation
A Computational Model of Lateralization and Asymmetries in Cortical Maps

Neural Computation
Alignment of coexisting cortical maps in a motor control model

Neural Computation
Plasticity-induced symmetry relationships between adjacent self-organizing topographic maps

Neural Computation
Paper: A neural model of cortical map reorganization following a focal lesion

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Past models of somatosensory cortex have successfullydemonstrated map formation and subsequent map reorganizationfollowing localized repetitive stimuli or deafferentation. Theyprovide an impressive demonstration that fairly simple assumptionsabout cortical connectivity and synaptic plasticity can account forseveral observations concerning cortical maps. However, past modelshave not successfully demonstrated spontaneous map reorganizationfollowing cortical lesions. Recently, an assumption universallyused in these and other cortex models, that peristimulus inhibitionis due solely to horizontal intracortical inhibitory connections,has been questioned and an additional mechanism, the competitivedistribution of activity, has been proposed. We implemented acomputational model of somatosensory cortex based on competitivedistribution of activity. This model exhibits spontaneous mapreorganization in response to a cortical lesion, going through atwo-phase reorganization process. These results make a testableprediction that can be used to experimentally support or refutepart of the competitive distribution hypothesis, and may lead topractically useful computational models of recovery followingstroke.