Patterns of damage in neural networks: The effects of lesion area, shape and number

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Eytan Ruppin, James Reggia


Current understanding of the effects of damage on neural networks is rudimentary, even though such understanding could lead to im(cid:173) portant insights concerning neurological and psychiatric disorders. Motivated by this consideration, we present a simple analytical framework for estimating the functional damage resulting from fo(cid:173) cal structural lesions to a neural network. The effects of focal le(cid:173) sions of varying area, shape and number on the retrieval capacities of a spatially-organized associative memory. Although our analyti(cid:173) cal results are based on some approximations, they correspond well with simulation results. This study sheds light on some important features characterizing the clinical manifestations of multi-infarct dementia, including the strong association between the number of infarcts and the prevalence of dementia after stroke, and the 'mul(cid:173) tiplicative' interaction that has been postulated to occur between Alzheimer's disease and multi-infarct dementia.

*Dr. Reggia is also with the Department of Neurology and the Institute of Advanced

Computer Studies at the University of Maryland.


Eytan Ruppin, James A. Reggia