Neuromorphic engineering
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Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain.[1][2] A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations.[3][4] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.[5]
The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories, threshold switches, transistors,[7][4] among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch,[8] or using canonical learning rules from the biological learning literature, e.g., using BindsNet.[9]
A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering[4] to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[10] One of the first applications for neuromorphic engineering was proposed by Carver Mead[11] in the late 1980s.