  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.
