Brain-Inspired Computing: Revolutionizing Medical Devices Through Neuromorphic Engineering, A Review of different options

Abstract

This document discusses brain-like computing and how it is likely to cause a shift in the way medical devices are treated. This is a direct result of pioneering such computing which is inspired by the ability of the human brain to surpass the energy and size limits of conventional computers. It is especially useful for devices that need to process and decide on data in real-time, which in turn enhances medical diagnoses and personalized healthcare. Brain-like systems open a whole new dimension to the processing of medical signals. Through the integration of these systems, we could have portable and wireless body area networks that eradicate complex offline processing tasks. They allow instantaneous analysis, which is essential for time-sensitive medical conditions where rapid feedback and interventions are required. This on-spot data analysis further reduces issues related to data loss or corruption, thereby providing accurate results. The trifecta of small power usage, quality of real-time processing, and high step of reliability in these circuits make the circuits particularly suitable for highly demanding medical applications. Neuromorphic systems present a promising avenue for biomedical applications, achieving energy efficiency through methods such as reduced signal sampling, which is viable given the sparsity of many biological signals. This approach aligns well with the requirements of energy-constrained systems and emulates the brain's efficient processing capabilities. Furthermore, transistors designed to mimic nerve connections offer the dual advantage of power conservation and biocompatibility, rendering them particularly suitable for devices intended for close interaction with biological tissues, while components with adaptable electrical resistance, akin to biological synapses, are essential for brain-inspired systems. The advent of artificial neurons that exhibit reduced power consumption and increased component density further enhances the potential of neuromorphic circuits, positioning them as a viable solution for creating compact and energy-efficient biomedical devices, for instance, one of the strategies includes devising circuits that would imitate the dynamic behavior of biological neurons to re-establish disrupted nerve communication. Devices that change their electrical resistance depending on the charge flow are a kind of connection that simulates how interconnections evolve, a key part of learning and memory in biological networks. The combination of smart processors has opened more opportunities for the algorithms to be introduced in healthcare and medical applications especially in a local processing context. Brain-like designs allow on-device signal processing at the nerve level and treatment, thus, becoming the brain-machine systems, which are personalized and responsive.