The effect of temporal pattern of injury on disability in learning networks
Neurons and Cognition
2016-12-12 v1 Disordered Systems and Neural Networks
Tissues and Organs
Abstract
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.
Cite
@article{arxiv.1205.2012,
title = {The effect of temporal pattern of injury on disability in learning networks},
author = {Mohammadkarim Saeedghalati and Abdolhossein Abbassian},
journal= {arXiv preprint arXiv:1205.2012},
year = {2016}
}
Comments
Latex, 17 pages, 7 figures, 2 tables