Modeling spatio-temporal dynamics of network damage and network recovery
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 networ...
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| Format: | Online |
| Language: | English |
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Frontiers Media S.A.
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4614320/ |
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pubmed-46143202015-11-09 Modeling spatio-temporal dynamics of network damage and network recovery Saeedghalati, Mohammadkarim Abbassian, Abdolhosein Neuroscience 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. Frontiers Media S.A. 2015-10-22 /pmc/articles/PMC4614320/ /pubmed/26557071 http://dx.doi.org/10.3389/fncom.2015.00130 Text en Copyright © 2015 Saeedghalati and Abbassian. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Saeedghalati, Mohammadkarim Abbassian, Abdolhosein |
| spellingShingle |
Saeedghalati, Mohammadkarim Abbassian, Abdolhosein Modeling spatio-temporal dynamics of network damage and network recovery |
| author_facet |
Saeedghalati, Mohammadkarim Abbassian, Abdolhosein |
| author_sort |
Saeedghalati, Mohammadkarim |
| title |
Modeling spatio-temporal dynamics of network damage and network recovery |
| title_short |
Modeling spatio-temporal dynamics of network damage and network recovery |
| title_full |
Modeling spatio-temporal dynamics of network damage and network recovery |
| title_fullStr |
Modeling spatio-temporal dynamics of network damage and network recovery |
| title_full_unstemmed |
Modeling spatio-temporal dynamics of network damage and network recovery |
| title_sort |
modeling spatio-temporal dynamics of network damage and network recovery |
| description |
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. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4614320/ |
| _version_ |
1613490856180842496 |