Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

TY - JOUR

T1 - Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

AU - Moore, Jasmine A

AU - Wilms, Matthias

AU - Gutierrez, Alejandro

AU - Ismail, Zahinoor

AU - Fakhar, Kayson

AU - Hadaeghi, Fatemeh

AU - Hilgetag, Claus C

AU - Forkert, Nils D

N1 - Copyright © 2023 Moore, Wilms, Gutierrez, Ismail, Fakhar, Hadaeghi, Hilgetag and Forkert.

PY - 2023

Y1 - 2023

N2 - The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico 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's 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's 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 in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

AB - The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico 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's 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's 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 in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

U2 - 10.3389/fncom.2023.1274824

DO - 10.3389/fncom.2023.1274824

M3 - SCORING: Journal article

C2 - 38105786

VL - 17

SP - 1274824

JO - FRONT COMPUT NEUROSC

JF - FRONT COMPUT NEUROSC

SN - 1662-5188

ER -