title: Modeling spatio-temporal dynamics of network damage and network recovery.
abstract: How networks endure damage is a central issue in neural network research. In this paper, we study the slow and fast dynamics of network damage and compare the results for two simple but very different models of recurrent and feed forward neural network. What we find is that a slower degree of network damage leads to a better chance of recovery in both types of network architecture. This is in accord with many experimental findings on the damage inflicted by strokes and by slowly growing tumors. Here, based on simulation results, we explain the seemingly paradoxical observation that disability caused by lesions, affecting large portions of tissue, may be less severe than the disability caused by smaller lesions, depending on the speed of lesion growth.
authors.affiliation: BioMath, School of Mathematics, Institute for Research in Fundamental Sciences (IPM) Tehran, Iran.
authors.forename: Mohammadkarim
authors.initials: M
authors.lastname: Saeedghalati
authors.affiliation: BioMath, School of Mathematics, Institute for Research in Fundamental Sciences (IPM) Tehran, Iran.
authors.forename: Abdolhosein
authors.initials: A
authors.lastname: Abbassian
keywords: brain damage; brain network models; fast dynamics; network damage; network dynamics; network recovery; slow dynamics; spatiotemporal dynamics