{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:07:24Z","timestamp":1768867644224,"version":"3.49.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based <jats:italic>in-silico<\/jats:italic> model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model\u2019s object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer\u2019s disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the <jats:italic>in-silico<\/jats:italic> setup that simulates neuroplasticity improves the model\u2019s biological feasibility considerably and could prove valuable to test different rehabilitation approaches <jats:italic>in-silico.<\/jats:italic><\/jats:p>","DOI":"10.3389\/fncom.2023.1274824","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T12:15:07Z","timestamp":1701432907000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system"],"prefix":"10.3389","volume":"17","author":[{"given":"Jasmine A.","family":"Moore","sequence":"first","affiliation":[]},{"given":"Matthias","family":"Wilms","sequence":"additional","affiliation":[]},{"given":"Alejandro","family":"Gutierrez","sequence":"additional","affiliation":[]},{"given":"Zahinoor","family":"Ismail","sequence":"additional","affiliation":[]},{"given":"Kayson","family":"Fakhar","sequence":"additional","affiliation":[]},{"given":"Fatemeh","family":"Hadaeghi","sequence":"additional","affiliation":[]},{"given":"Claus C.","family":"Hilgetag","sequence":"additional","affiliation":[]},{"given":"Nils D.","family":"Forkert","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.neunet.2019.04.021","article-title":"Redundant feature pruning for accelerated inference in deep neural networks","volume":"118","author":"Ayinde","year":"2019","journal-title":"Neural Netw."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e1003963","DOI":"10.1371\/journal.pcbi.1003963","article-title":"Deep neural networks rival the representation of primate IT cortex for core visual object recognition","volume":"10","author":"Cadieu","year":"2014","journal-title":"PLoS Comput. 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