Dynamic Neuro-Morphic Network Architecture Adaptation

Abstract

This paper presents a novel approach to designing neuro-morphic networks capable of adapting to dynamic and complex input data. The core idea revolves around mimicking the inherent plasticity and evolving connectivity observed in biological neural networks. We introduce a reinforcement learning-based algorithm that continuously monitors input data statistics and network performance, dynamically adjusting the synaptic strengths and connection weights between neurons to optimize response speed and accuracy. The system leverages biologically-inspired neuron models, such as pulse and dynamic membrane models, to simulate neuronal behavior accurately. Unlike conventional neuro-morphic networks which often rely on fixed hardware architectures, our approach focuses on software-based adaptation, offering a more flexible and efficient solution. The key innovation lies in the integration of reinforcement learning and bio-inspired models, creating a network that learns and adapts in real-time. This adaptive architecture demonstrates potential for applications in sensor data processing, robotics, and pattern recognition where input data characteristics change over time. The overall framework addresses the limitations of static neuro-morphic designs by providing a dynamic and intelligent network capable of handling unpredictable environments.