The effect of temporal pattern of injury on disability in learning   networks
  How networks endure damage is a central issue in neural network research. This includes temporal as well as spatial pattern of damage. Here, based on some very simple models we study the difference between a slow-growing and acute damage and the relation between the size and rate of injury. Our result shows that in both a three-layer and a homeostasis model a slow-growing damage has a decreasing effect on network disability as compared with a fast growing one. This finding is in accord with clinical reports where the state of patients before and after the operation for slow-growing injuries is much better that those patients with acute injuries. 
Mohammadkarim Saeedghalati, Abdolhossein Abbassian
q-bio.NC cond-mat.dis-nn q-bio.TO