The effect of temporal pattern of injury on disability in learning networks

Autor:	Saeedghalati, Mohammadkarim, Abbassian, Abdolhossein
Rok vydání:	2012
Předmět:	Quantitative Biology - Neurons and Cognition Condensed Matter - Disordered Systems and Neural Networks Quantitative Biology - Tissues and Organs
Druh dokumentu:	Working Paper
DOI:	10.3389/fncom.2015.00130
Popis:	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. Comment: Latex, 17 pages, 7 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1205.2012 Zobrazit plný text záznamu View this record from Arxiv