2012/05/09 by Mohammadkarim Saeedghalati, Abdolhossein Abbassian, Abdolhosein Abbassian
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Artificial intelligence #Artificial neural network #Cell Image Analysis Techniques #Computer network #Computer science #Dynamics (music) #Lesion #Mathematics #Network architecture #Network dynamics #Network model #Neural Networks and Applications #Neural dynamics and brain function #Neuroscience #Psychology #Recurrent neural network #cond-mat.dis-nn #q-bio.NC #q-bio.TO
paper · pdf · doi:10.3389/fncom.2015.00130
published in Frontiers in Computational Neuroscience 9, 130 (Frontiers Media) · Latex, 17 pages, 7 figures, 2 tables
arxiv created 2012/05/09 · openalex publication_date 2015/10/22 · arxiv updated 2016/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.