Neural Network Dynamics: Topology Self-Organization

Abstract

This paper investigates the potential of dynamically self-organizing neural networks inspired by biological systems. Traditional static neural networks often struggle with adaptability and efficiency in complex environments. We propose a novel approach that leverages reinforcement learning to dynamically adjust connection strengths and incorporates probabilistic mechanisms for synaptic generation and removal, mirroring the plasticity observed in biological neural networks. The network topology itself evolves over time, driven by the reinforcement learning process, leading to the formation of more effective connection patterns. Our simulations demonstrate that this dynamic topology self-organization significantly enhances learning and inference capabilities compared to static networks. The core claim is that by simulating dynamic connections and synaptic changes, we can achieve more efficient and adaptive learning and reasoning. The central mechanism utilizes reinforcement learning to adjust connection strengths based on network output success rates and introduces probabilistic synaptic generation and deletion to mimic biological synaptic plasticity. The network topology evolves over time, creating optimized connection patterns.