Neuromorphic computing

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] An article published by AI researchers at Los Alamos National Laboratory states that, "neuromorphic computing, the next generation of AI, will be smaller, faster, and more efficient than the human brain."^[6]

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.^[7] One of the first applications for neuromorphic engineering was proposed by Carver Mead^[8] in the late 1980s.

^ Ham, Donhee; Park, Hongkun; Hwang, Sungwoo; Kim, Kinam (2021). "Neuromorphic electronics based on copying and pasting the brain". Nature Electronics. 4 (9): 635–644. doi:10.1038/s41928-021-00646-1. ISSN 2520-1131. S2CID 240580331.
^ van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto (April 2017). "A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing". Nature Materials. 16 (4): 414–418. Bibcode:2017NatMa..16..414V. doi:10.1038/nmat4856. ISSN 1476-4660. PMID 28218920.
^ Mead, Carver (1990). "Neuromorphic electronic systems" (PDF). Proceedings of the IEEE. 78 (10): 1629–1636. doi:10.1109/5.58356. S2CID 1169506.
^ ^a ^b Cite error: The named reference :2 was invoked but never defined (see the help page).
^ Tomassoli, Laura; Silva-Dias, Leonardo; Dolnik, Milos; Epstein, Irving R.; Germani, Raimondo; Gentili, Pier Luigi (February 8, 2024). "Neuromorphic Engineering in Wetware: Discriminating Acoustic Frequencies through Their Effects on Chemical Waves". The Journal of Physical Chemistry B. 128 (5): 1241–1255. doi:10.1021/acs.jpcb.3c08429. ISSN 1520-6106. PMID 38285636.
^ Dickman, Kyle. "Neuromorphic computing: the future of AI | LANL". Kyle Dickman. Retrieved April 16, 2025.
^ Boddhu, S. K.; Gallagher, J. C. (2012). "Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers". Applied Computational Intelligence and Soft Computing. 2012: 1–21. doi:10.1155/2012/705483.
^ Mead, Carver A.; Mahowald, M. A. (January 1, 1988). "A silicon model of early visual processing %2888%2990024-X". Neural Networks. 1 (1): 91–97. doi:10.1016/0893-6080(88)90024-X. ISSN 0893-6080.

[1] Ham, Donhee; Park, Hongkun; Hwang, Sungwoo; Kim, Kinam (2021). "Neuromorphic electronics based on copying and pasting the brain". Nature Electronics. 4 (9): 635–644. doi:10.1038/s41928-021-00646-1. ISSN 2520-1131. S2CID 240580331.

[2] van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto (April 2017). "A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing". Nature Materials. 16 (4): 414–418. Bibcode:2017NatMa..16..414V. doi:10.1038/nmat4856. ISSN 1476-4660. PMID 28218920.

[3] Mead, Carver (1990). "Neuromorphic electronic systems" (PDF). Proceedings of the IEEE. 78 (10): 1629–1636. doi:10.1109/5.58356. S2CID 1169506.

[:2-4] Cite error: The named reference :2 was invoked but never defined (see the help page).

[5] Tomassoli, Laura; Silva-Dias, Leonardo; Dolnik, Milos; Epstein, Irving R.; Germani, Raimondo; Gentili, Pier Luigi (February 8, 2024). "Neuromorphic Engineering in Wetware: Discriminating Acoustic Frequencies through Their Effects on Chemical Waves". The Journal of Physical Chemistry B. 128 (5): 1241–1255. doi:10.1021/acs.jpcb.3c08429. ISSN 1520-6106. PMID 38285636.

[6] Dickman, Kyle. "Neuromorphic computing: the future of AI | LANL". Kyle Dickman. Retrieved April 16, 2025.

[7] Boddhu, S. K.; Gallagher, J. C. (2012). "Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers". Applied Computational Intelligence and Soft Computing. 2012: 1–21. doi:10.1155/2012/705483.

[8] Mead, Carver A.; Mahowald, M. A. (January 1, 1988). "A silicon model of early visual processing %2888%2990024-X". Neural Networks. 1 (1): 91–97. doi:10.1016/0893-6080(88)90024-X. ISSN 0893-6080.

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