Adaptive Neuron Topology Optimization

Abstract

This paper introduces a novel approach to neural network design termed Adaptive Neuron Topology Optimization. The core concept revolves around dynamically adjusting the connectivity topology of a neural network based on real-time monitoring of neuron activity. Traditional neural network topology is static, often hindering optimal performance. This work proposes a system leveraging reinforcement learning to optimize the network's topology. Specifically, a reinforcement learning algorithm is employed to modify both connection weights and the connections themselves between neurons, creating a self-adapting topology. The system aims to improve learning efficiency and generalization capabilities by allowing the network to evolve its structure based on the data it is processing. The key innovation lies in the dynamic, data-driven adaptation of the network topology, moving away from pre-defined static architectures. The system's effectiveness is demonstrated through a theoretical framework outlining the core mechanisms and potential benefits.