Neuro-Topology Adaptive Dynamics

Abstract

This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Policy Optimization (PPO), we can significantly improve network efficiency and generalization capabilities. NTAD employs a "Neuro-Topology Evaluator" – a neural network itself – to continuously assess the performance of the current connection graph, considering metrics such as accuracy, latency, and energy consumption. The PPO algorithm then adjusts connection weights and introduces or removes neurons to maximize the evaluator's score. Unlike traditional training methods relying on static graphs and fixed optimization targets, NTAD offers a dynamic, self-optimizing solution capable of handling non-static, high-dimensional input data and autonomously discovering optimal neural connection patterns. The methodology presented here represents a significant advancement in neural network design, shifting the focus from solely adjusting weights to actively shaping the network's physical structure.