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

Understanding the response of neural nets to structural/functional damage is important fora variety of reasons, e.g., in assessing the performance of neural network hardware, and in gaining understanding of the mechanisms underlying neurological andpsychiatric disorders. Recently, there has been a growing interest in constructing neuralmodels to study how specific pathological neuroanatomical and neurophysiological changes can result in various clinical manifestations, and to investigate thefunctional organization of the symptoms that result from specific brain pathologies (reviewed in [1, 2]). In the area of associative memory models specifically, earlystudies found an increase in memory impairment with increasing lesion severity (in accordance with Lashley's classical'mass action' principle), and showed that slowly developing lesions have less pronounced effects than equivalent acute lesions [3]. Recently, it was shown that the gradual pattern of clinical deterioration manifested in the majority of Alzheimer's patients can be accounted for, and that different synaptic compensation rates can account for the observed variation in the severity and progression rate of this disease [4]. However, this past work is limited in that model elements have no spatial relationships to one another (all elements are conceptually equidistant).