Nonlinear control of neurodegenerative diseases. A case study on optical illusion networks disrupted by diabetic retinopathy
File(s)
Published version
Author(s)
Giannari, AG
Astolfi, A
Type
Journal Article
Abstract
We present an efficient computational framework for the design of optimal drug delivery control strategies that can successfully treat a family of neurodegenerative diseases that originate from channelopathies and synaptopathies. To this end, we extend our previously introduced scalable and adaptable modelling framework that models heterogeneous Hodgkin–Huxley (HH) neuronal networks to account for the modular organisation of the neurons in the brain, e.g. interconnecting sub-networks of heterogeneous neurons. Based on this framework, we introduce a novel design of lateral inhibition networks to successfully reproduce 2D optical illusions that are known to occur in the human retina. We model the dynamic behaviour of Diabetic Retinopathy (DR), a neurodegenerative disease that progressively hinders the inherent ability of patients to distinguish optical illusions. We implement nonlinear control on accurate models of diabetic lateral inhibition neuronal networks to recover their functionality and investigate the effects of virtual drug administration. We utilise the healthy and diabetic optical illusions generated by these networks as a ‘computational’ phenotype to design therapies based on an adaptive terminal error iterative learning controller (TE-ILC). Therefore, we provide a comprehensive computational framework that is able to imitate the dynamics of healthy and diseased neuronal networks and we propose an adaptive nonlinear control strategy based on the error between output images that correspond to healthy and diseased conditions.
Date Issued
2024-02-07
Date Acceptance
2023-12-11
Citation
Neurocomputing, 2024, 569
ISSN
0925-2312
Publisher
Elsevier
Journal / Book Title
Neurocomputing
Volume
569
Copyright Statement
© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.1016/j.neucom.2023.127099
Publication Status
Published
Article Number
127099
Date Publish Online
2023-12-16