Dynamic Neural Network Topology (DNTN)

Abstract

This paper introduces the Dynamic Neural Network Topology (DNTN), a novel neural network architecture designed to overcome the limitations of static, connection-based networks. The core claim of this work is that by dynamically adjusting the physical connection strengths and topology of neurons in real-time, adaptive learning and memory capabilities can be achieved, surpassing the constraints of traditional neural networks. The proposed DNTN utilizes a Microelectromechanical Systems (MEMS) array as a neuron substrate, with each MEMS structure representing a neuron. A reinforcement learning controller dynamically adjusts connection parameters based on task objectives and environmental feedback, optimizing network structure and function. Furthermore, a metabolic module mimics biological neuron energy consumption, preventing excessive connections and network degradation. The DNTN represents a significant advancement in neural network design, offering enhanced learning efficiency, flexibility, and a closer simulation of biological neural systems. Key characteristics include dynamic topology reconfiguration, real-time adaptation, and a biologically inspired metabolic control mechanism.