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Publication
A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healing
Advisor(s)
Abstract(s)
Alzheimer’s disease (AD) is a degenerative neurological condition that impacts millions of individuals across the globe and remains without a healing. In the search for new possibilities of treatments for this terrible disease, this work presents the improved Alzheimer-like disease (IALD) model for memory failure and connects it to a new control technique that establishes a cure for the memory lost, either in biological or in artificial neural networks. For the IALD model, continuous Hopfield neural networks (HNN) with time delay are used. From the healing side, a robust control technique is used, which is based on new discoveries in Lurie control systems. In addition, this paper reviews the development of Alzheimer-like disease (ALD) model, as well as, the relationship of HNN with Lurie system. Simulations are executed to validate the model and to show the efficacy of applying a new theorem from Lurie problem. With the results presented, this work proposes a new conceptual paradigm that could potentially be applied in memory failure treatments in AD, as well as in hardware implemented HNN under adversarial attacks or adverse environmental conditions.
Description
Article number - 129967
Keywords
Alzheimer disease DK-iteration Hopfield neural network Lurie type systems Neuroscience Robust control
Pedagogical Context
Citation
Rafael Fernandes Pinheiro, Diego Colón, Rui Fonseca-Pinto, A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healing, Neurocomputing, Volume 636, 2025, 129967, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2025.129967.
Publisher
Elsevier