A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healing

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. • A novel control for Alzheimer-like disease (ALD) via Lurie problem theory (LP). • First use of LP-based controllers in an improved ALD model with delayed networks. • Integrates LP, robust control, and neural networks (ANN) for ALD management. • Bridges modeling, control theory, ANN, and neuroscience. • This approach may enable future therapy for neurodegenerative diseases.