AUTHOR=Saeedghalati Mohammadkarim , Abbassian Abdolhosein TITLE=Modeling spatio-temporal dynamics of network damage and network recovery JOURNAL=Frontiers in Computational Neuroscience VOLUME=9 YEAR=2015 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2015.00130 DOI=10.3389/fncom.2015.00130 ISSN=1662-5188 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.