Dynamic Neuron Topology Mapping and Adaptive Neuron Connection Generation

Abstract

This paper proposes a novel framework for dynamically mapping neuron topologies and generating adaptive neuron connections within neural networks. The core idea is to integrate topological network modeling with reinforcement learning to create a system capable of learning and adapting to complex neural network topologies. The system employs a multi-level modeling approach. The first level utilizes topological networks such as small-world networks and scale-free networks to represent the neural network. The second level leverages reinforcement learning algorithms, such as policy gradient or Actor-Critic, to train the connection strength between neurons. Crucially, connection strength is determined not only by neuron input/output activity but also by local topological information, including connection density and distance. A third level, an adaptive topology generation module, dynamically alters the topology structure based on network performance and learning objectives, adding or removing neurons and modifying their connections. This approach aims to optimize information processing and learning efficiency by mitigating information bottlenecks and accelerating signal propagation. The system demonstrates the potential to overcome limitations of existing neural network modeling methods and address complex learning and information processing challenges in neural networks.