Adaptive Neuron Growth Networks

Abstract

This paper introduces Adaptive Neuron Growth Networks (ANGNs), a novel neural network architecture designed for dynamic adaptation to varying input complexities and network performance. ANGNs utilize a reinforcement learning-based mechanism where individual neurons dynamically adjust their parameters – both synaptic weights and intrinsic neuron properties – based on their output error and the overall state of the network. Unlike traditional adaptive learning approaches that primarily focus on parameter tuning, ANGNS introduce a core mechanism for self-organization and structural modification, enabling the network to grow or prune neurons and connections proportionally to the complexity of the input data and the efficiency of the network's operation. The key innovation lies in the integration of reinforcement learning with a network growth/pruning strategy, allowing for a truly adaptive and scalable neural network architecture. The theoretical framework and the proposed algorithm are presented, demonstrating the potential of ANGNS for tasks requiring adaptability and robustness.