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arXiv · 1205.2012

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

Mohammadkarim Saeedghalati·Abdolhossein Abbassian
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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.

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Mohammadkarim Saeedghalati, Abdolhossein Abbassian. 2012-05-09. The effect of temporal pattern of injury on disability in learning networks. https://doi.org/10.3389/fncom.2015.00130

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