Modeling spatio-temporal dynamics of network damage and network recovery - Saeedghalati, Mohammadkarim; Abbassian, Abdolhosein (Frontiers in Computational Neuroscience, 2015)
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 such as a damage inflicted on stroke patients as compared to a slow growing tumor. Here, based on simulation results we explain the seemingly paradoxical observation that a larger size of local tissue damage causes a less severe disability than a smaller size, depending on the speed of damage.