The role of neuromorphic principles in the future of biomedicine and healthcare

Grace M. Hwang; Jessica D. Falcone; Joseph D. Monaco; Courtney R. Pinard; Jessica A. Mollick; Roger L. Miller; Stephanie L. Gage; Andrey V. Kanaev

cs.NEMar 29, 2026

The role of neuromorphic principles in the future of biomedicine and healthcare

Authors: Grace M. Hwang, Jessica D. Falcone, Joseph D. Monaco, Courtney R. Pinard, Jessica A. Mollick, Roger L. Miller, Stephanie L. Gage, Andrey V. Kanaev,

Organizations: National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), The Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, Bethesda, MD 20892, USA · National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD 20892, USA · National Institute of Mental Health (NIMH), NIH, Bethesda, MD 20892, USA · National Institute of Drug Abuse (NIDA), NIH, Bethesda, MD 20892, USA ·

Abstract

Neuromorphic engineering has matured over the past four decades and is currently experiencing explosive growth with the potential to transform biomedical engineering and neurotechnologies. Participants at the Neuromorphic Principles in Biomedicine and Healthcare (NPBH) Workshop (October 2024) -- representing a broad cross-section of the community, including early-career and established scholars, engineers, scientists, clinicians, industry, and funders -- convened to discuss the state of the field, current and future challenges, and strategies for advancing neuromorphic research and development for biomedical applications. Publicly approved recordings with transcripts (https://2024.neuro-med.org/program/session-video-and-transcripts) and slides (https://2024.neuro-med.org/program/session-slides) can be found at the workshop website.

Neuromorphic Computing

Neuromorphic computing uses brain-inspired, event-driven algorithms and hardware to process information with low power and latency.

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Sensing to Intelligence: Principles for Neuromorphic Circuits and Systems

Saptarshi Maiti, Chetan Singh Thakur

Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, 560012, India. · Brain, Computation, and Data Science, Indian Institute of Science, Bangalore, 560012, India.

Neuromorphic Computing

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Neuromorphic engineering began with the idea that the physical behavior of a system could itself be used for computation, taking inspiration from the way nervous systems sense, adapt, and evolve in time. The field has since expanded far beyond its early analog circuits to include event-based sensors, spiking processors, emerging memory devices, mixed-signal systems, and large-scale neural accelerators. With this expansion, however, the meaning of neuromorphic has become increasingly broad. In this Perspective, we argue that neuromorphic engineering should be defined neither by resemblance to biological components nor by any particular device, signal representation, or substrate. Its potential lies in identifying computational principles in biological systems and translating them into the organization and dynamics of artificial machines. A system is truly neuromorphic when the invoked biological or physical principle plays a causal, design-relevant role in how information is represented, how state evolves, or what capability the complete system achieves. We develop this view across sensing, collective computation, memory, learning, and interaction. Adaptation, nonlinear dynamics, attractor structure, variability, and closed-loop action illustrate how computation can be embedded in a machine's evolving physical state. From these examples, we identify a set of design principles for neuromorphic systems. As neuromorphic engineering moves toward wider deployment, this perspective shifts the central question from how closely machines resemble nervous systems to what computation becomes possible when their principles are understood and deliberately translated into new machines.

Abstract

Neuromorphic Computing

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