Version 1
: Received: 21 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (08:05:36 CEST)
How to cite:
Zhang, L. Dynamic Analysis and Control of Neural Networks: Stability, Oscillation, and Weight-Based Modulation. Preprints2023, 2023051609. https://doi.org/10.20944/preprints202305.1609.v1
Zhang, L. Dynamic Analysis and Control of Neural Networks: Stability, Oscillation, and Weight-Based Modulation. Preprints 2023, 2023051609. https://doi.org/10.20944/preprints202305.1609.v1
Zhang, L. Dynamic Analysis and Control of Neural Networks: Stability, Oscillation, and Weight-Based Modulation. Preprints2023, 2023051609. https://doi.org/10.20944/preprints202305.1609.v1
APA Style
Zhang, L. (2023). Dynamic Analysis and Control of Neural Networks: Stability, Oscillation, and Weight-Based Modulation. Preprints. https://doi.org/10.20944/preprints202305.1609.v1
Chicago/Turabian Style
Zhang, L. 2023 "Dynamic Analysis and Control of Neural Networks: Stability, Oscillation, and Weight-Based Modulation" Preprints. https://doi.org/10.20944/preprints202305.1609.v1
Abstract
This paper investigates the dynamic properties of artificial neural networks using differential equations and explores the influence of parameters on stability and neural oscillations. By analyzing the equilibrium point of the differential equations, we identify conditions for asymptotic stability and criteria for oscillation in artificial neural networks. Furthermore, we demonstrate how adjusting synaptic weights between neurons can effectively control stability and oscillation. The proposed model offers potential insights into the malfunctioning mechanisms of biological neural networks implicated in neurological disorders like Parkinson's disease tremors and epilepsy seizures, which are characterized by abnormal oscillations.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
