Article
Version 1
Preserved in Portico This version is not peer-reviewed
Simple Dynamic Visualization of Memristor-Based Synaptic Plasticity in a Simulated Neural Network
Version 1
: Received: 17 May 2024 / Approved: 17 May 2024 / Online: 17 May 2024 (15:40:02 CEST)
How to cite: Montgomery, R. Simple Dynamic Visualization of Memristor-Based Synaptic Plasticity in a Simulated Neural Network. Preprints 2024, 2024051176. https://doi.org/10.20944/preprints202405.1176.v1 Montgomery, R. Simple Dynamic Visualization of Memristor-Based Synaptic Plasticity in a Simulated Neural Network. Preprints 2024, 2024051176. https://doi.org/10.20944/preprints202405.1176.v1
Abstract
This article presents a novel computational approach to visualizing memristor dynamics within a simulated neural network. Memristors, known for their ability to emulate synaptic plasticity due to their variable resistance characteristics, are key components in neuromorphic computing and hold potential for advancing our understanding of neural processes. Our study introduces a sophisticated memristor model that incorporates non-linear resistance changes, simulating the complex behavior of synaptic connections in a neural network.The neural network, consisting of multiple interconnected neurons with memristor-based synapses, is subjected to a series of electrical stimuli. Each memristor's resistance is modulated in response to the applied voltage, mimicking the synaptic weight adjustments that occur during learning and memory formation in biological neural networks. To effectively illustrate these changes, we employ a high-contrast color mapping scheme, where the varying resistance of each memristor is represented by distinct colors, providing a clear and intuitive visualization of synaptic modifications over time.Our simulation runs through multiple iterations, demonstrating how synaptic weights evolve in response to different input patterns. The use of an extended voltage range and increased scaling factors ensures pronounced changes in memristance, enhancing the visibility of synaptic adaptations. The resulting visualizations offer a compelling representation of how memristors can mimic the dynamic nature of biological synapses, contributing to the field of neuromorphic engineering and deepening our comprehension of neural mechanisms underlying learning and memory.This work not only showcases the potential of memristors in simulating neural behavior but also provides a valuable educational tool for illustrating complex concepts in neuroscience and neuromorphic computing. The insights gained from this study pave the way for further exploration into the development of advanced neural network models and the design of memristor-based computing systems.
Keywords
memristor; synaptic plasticity; neural network; neuromorphic computing
Subject
Biology and Life Sciences, Neuroscience and Neurology
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.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment