Summary
Computers can work very differently by copying how the brain senses and thinks, but not by copying its parts directly. The authors argue that neuromorphic engineering means using ideas from biology to design how a machine processes information and changes over time. They show that machines are truly neuromorphic when these biological ideas affect how information is stored, processed, and learned. The paper explains key principles like adaptation and nonlinear dynamics that help create machines that compute through changing physical states. This viewpoint shifts focus from copying brains exactly to understanding and using their ideas to build new kinds of machines.
What this means in practice
- •For hardware engineers: Design circuits and systems that embody biological principles to enhance computing adaptability and efficiency in sensors and processors.
- •For robotics developers: Create robots that use neuromorphic principles for real-time sensing, learning, and interaction through evolving physical states.
A position paper. It proposes an approach and reports no results.
Abstract
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.