Neuromorphic and Bio-Inspired Computing for Intelligent Healthcare Networks

Abstract

Spiking Neural Networks and bio-inspired computing systems have come up as viable technologies that can revolutionize healthcare networks by offering effective, scalable, and real-time medical data processing solutions. This article discusses the unification of Spiking Neural Networks and memristor-based learning into healthcare applications, including real-time monitoring of patients, disease prognosis, and tailored treatment protocols. The suggested techniques provide significant energy efficiency gains, ranging as low as 0.3 milliwatts per operation while preserving processing rates of 2.0 milliseconds. System performance is measured on key parameters such as accuracy (up to 93.0%) and system reliability (with 99.2% uptime). Bio-inspired optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are also employed for resource planning and treatment planning. These algorithms yield effective solutions in dynamic healthcare settings. Nevertheless, there are challenges such as hardware limitations and improved algorithms required. The combination of these neuromorphic systems holds a major boost in healthcare efficiency, guaranteeing the provision of timely diagnostics, personalized medicine, and secure data communication with 1300.8 bps throughput. This paper addresses how neuromorphic and bio-inspired computer systems hold the key to meeting the increasing demands for smart healthcare solutions through their scalability, flexibility, and real-time processing.