Self-Organizing Neural Networks for Dynamic Topology Learning

Abstract

This paper presents a novel approach to neural network design utilizing self-organizing principles to dynamically learn network topology. The core idea involves constructing a neural network architecture capable of adapting its structure in response to incoming data and its internal state. This adaptation is achieved through a feedback loop mechanism governed by a reinforcement learning paradigm. Neurons adjust their connections based on activation patterns and error signals, iteratively refining the network's structure to optimize performance. The resultant networks exhibit data-driven learning of topology, moving beyond static, pre-defined architectures. This approach offers significant potential in scenarios where the underlying data structure is unknown or constantly evolving, leading to more efficient and robust neural networks. The primary mathematical framework focuses on the recurrent dynamics of the network, expressed through differential equations and Markov chain analysis, to model the evolving connection weights and neuron activation states. Key performance metrics, such as the mean squared error (MSE) and the convergence rate, are employed to evaluate the effectiveness of the learning process.