Neuromorphic computing

{{Short description|Integrated circuit technology}} {{Use American English|date = January 2019}} {{Use mdy dates|date = January 2019}} {{Machine learning|Paradigms}} {{Infobox technology | name = Neuromorphic computing | image = | caption = | inventor = Carver Mead | invented = Late 1980s | purpose = Mimics brain structure and function for efficient computing | applications = Artificial intelligence, pattern recognition, sensory processing }}

'''Neuromorphic computing''' is a computing approach inspired by the human brain's structure and function.<ref>{{Cite journal |last1=Ham |first1=Donhee |last2=Park |first2=Hongkun |last3=Hwang |first3=Sungwoo |last4=Kim |first4=Kinam |title=Neuromorphic electronics based on copying and pasting the brain |url=https://www.nature.com/articles/s41928-021-00646-1 |journal=Nature Electronics |year=2021 |language=en |volume=4 |issue=9 |pages=635–644 |doi=10.1038/s41928-021-00646-1 |s2cid=240580331 |issn=2520-1131|url-access=subscription }}</ref><ref>{{Cite journal |last1=van de Burgt |first1=Yoeri |last2=Lubberman |first2=Ewout |last3=Fuller |first3=Elliot J. |last4=Keene |first4=Scott T. |last5=Faria |first5=Grégorio C. |last6=Agarwal |first6=Sapan |last7=Marinella |first7=Matthew J. |last8=Alec Talin |first8=A. |last9=Salleo |first9=Alberto |date=April 2017 |title=A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing |url=https://www.nature.com/articles/nmat4856 |journal=Nature Materials |language=en |volume=16 |issue=4 |pages=414–418 |doi=10.1038/nmat4856 |pmid=28218920 |bibcode=2017NatMa..16..414V |issn=1476-4660|hdl=11370/c5352651-9a4a-40e2-b256-c54b844a89ea |hdl-access=free }}</ref> It uses artificial neurons to perform computations, mimicking neural systems for tasks such as perception, motor control, and multisensory integration.<ref>{{cite journal|last1=Mead|first1=Carver|title=Neuromorphic electronic systems|journal=Proceedings of the IEEE|date=1990|volume=78|issue=10|pages=1629–1636|doi=10.1109/5.58356|s2cid=1169506 |url=https://authors.library.caltech.edu/53090/1/00058356.pdf}}</ref> These systems, implemented in analog, digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing elements.<ref>{{Cite journal | doi = 10.1155/2012/705483| title = Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers| journal = Applied Computational Intelligence and Soft Computing| volume = 2012| pages = 1–21| year = 2012| last1 = Boddhu | first1 = S. K. | last2 = Gallagher | first2 = J. C. | doi-access = free}}</ref> This interdisciplinary field integrates biology, physics, mathematics, computer science, and electronic engineering to develop systems that emulate the brain’s morphology and computational strategies.<ref>{{Cite conference|author1=Rami A. Alzahrani|author2=Alice C. Parker|title=Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling |conference=International Conference on Neuromorphic Systems 2020|date=July 2020|pages=1–8|language=EN|doi=10.1145/3407197.3407204|s2cid=220794387|doi-access=free}}</ref> Neuromorphic systems aim to enhance energy efficiency and computational power for applications including artificial intelligence, pattern recognition, and sensory processing. == History == Carver Mead proposed one of the first applications for neuromorphic engineering in the late 1980s.<ref>{{Cite journal |last1=Mead |first1=Carver A. |last2=Mahowald |first2=M. A. |date=1988-01-01 |title=A silicon model of early visual processing %2888%2990024-X |journal=Neural Networks |language=en |volume=1 |issue=1 |pages=91–97 |doi=10.1016/0893-6080(88)90024-X |issn=0893-6080|url=https://resolver.caltech.edu/CaltechAUTHORS:20141223-110732666 }}</ref> In 2006, researchers at Georgia Tech developed a field programmable neural array, a silicon-based chip modeling neuron channel-ion characteristics.<ref>{{Cite book|last1 = Farquhar|first1 = Ethan|date = May 2006|pages = 4114–4117|last2 = Hasler|first2 = Paul.| title=2006 IEEE International Symposium on Circuits and Systems | chapter=A Field Programmable Neural Array |doi = 10.1109/ISCAS.2006.1693534|isbn = 978-0-7803-9389-9|s2cid = 206966013}}</ref> In 2011, MIT researchers created a chip mimicking synaptic communication using 400 transistors and standard CMOS techniques.<ref>{{cite web|title=MIT creates "brain chip"|date=November 15, 2011 |url=http://www.extremetech.com/extreme/105067-mit-creates-brain-chip|access-date=4 December 2012}}</ref><ref>{{cite journal|title=Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities|doi=10.3389/fnins.2011.00108|pmid=21991244|pmc=3181466|volume=5|page=108|journal=Frontiers in Neuroscience|year=2011|last1=Poon|first1=Chi-Sang|last2=Zhou|first2=Kuan|doi-access=free}}</ref>

In 2012 HP Labs researchers reported that Mott memristors exhibit volatile behavior at low temperatures, enabling the creation of neuristors that mimic neuron behavior and support Turing machine components.<ref>{{cite journal |last1=Pickett |first1=M. D. |last2=Medeiros-Ribeiro |first2=G. |last3=Williams |first3=R. S. |year=2012 |title=A scalable neuristor built with Mott memristors |journal=Nature Materials |volume=12 |issue=2 |pages=114–7 |bibcode=2013NatMa..12..114P |doi=10.1038/nmat3510 |pmid=23241533 |s2cid=16271627}}</ref> Also in 2012, Purdue University researchers presented a neuromorphic chip design using lateral spin valves and memristors, noted for energy efficiency.<ref>{{Cite arXiv|title=Proposal For Neuromorphic Hardware Using Spin Devices|eprint=1206.3227|last1=Sharad|first1=Mrigank|last2=Augustine|first2=Charles|last3=Panagopoulos|first3=Georgios|last4=Roy|first4=Kaushik|class=cond-mat.dis-nn|year=2012}}</ref>

The 2013 Blue Brain Project creates detailed digital models of rodent brains.<ref>{{cite web |title=Involved Organizations |url=http://www.humanbrainproject.eu/partners.html |archive-url=https://web.archive.org/web/20130302142627/http://www.humanbrainproject.eu/partners.html |archive-date=2 March 2013 |access-date=22 February 2013}}</ref>

Neurogrid, developed by ''Brains in Silicon'' at Stanford University, used 16 NeuroCore chips to emulate 65,536 neurons with high energy efficiency in 2014.<ref>{{cite journal |last1=Boahen |first1=Kwabena |date=24 April 2014 |title=Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations |journal=Proceedings of the IEEE |volume=102 |issue=5 |pages=699–716 |doi=10.1109/JPROC.2014.2313565 |s2cid=17176371}}</ref> The 2014 BRAIN Initiative and IBM’s TrueNorth chip contributed to neuromorphic advancements.<ref>{{cite journal |last1=Modha |first1=Dharmendra |date=Aug 2014 |title=A million spiking-neuron integrated circuit with a scalable communication network and interface |journal=Science |volume=345 |issue=6197 |pages=668–673 |bibcode=2014Sci...345..668M |doi=10.1126/science.1254642 |pmid=25104385 |s2cid=12706847}}</ref>

The 2016 BrainScaleS project, a hybrid neuromorphic supercomputer at University of Heidelberg, operated 864 times faster than biological neurons.<ref>{{Cite web |date=2016-03-21 |title=Beyond von Neumann, Neuromorphic Computing Steadily Advances |url=https://www.hpcwire.com/2016/03/21/lacking-breakthrough-neuromorphic-computing-steadily-advance/ |access-date=2021-10-08 |website=HPCwire |language=en-US}}</ref>

In 2017, Intel unveiled its Loihi chip, using an asynchronous artificial neural network for efficient learning and inference.<ref>{{cite journal |last1=Davies |first1=Mike |display-authors=etal |date=January 16, 2018 |title=Loihi: A Neuromorphic Manycore Processor with On-Chip Learning |journal=IEEE Micro |volume=38 |issue=1 |pages=82–99 |bibcode=2018IMicr..38a..82D |doi=10.1109/MM.2018.112130359 |s2cid=3608458}}</ref> Also in 2017 IMEC’s self-learning chip, based on OxRAM, demonstrated music composition by learning from minuets.<ref>{{cite web |last1=Bourzac |first1=Katherine |date=May 23, 2017 |title=A Neuromorphic Chip That Makes Music |url=https://spectrum.ieee.org/a-neuromorphic-chip-that-makes-music |access-date=1 October 2019 |website=IEEE Spectrum}}</ref>

In 2022, MIT researchers developed artificial synapses using protons for analog deep learning.<ref>{{cite journal |last1=Onen |first1=Murat |last2=Emond |first2=Nicolas |last3=Wang |first3=Baoming |last4=Zhang |first4=Difei |last5=Ross |first5=Frances M. |last6=Li |first6=Ju |last7=Yildiz |first7=Bilge |last8=del Alamo |first8=Jesús A. |date=29 July 2022 |title=Nanosecond protonic programmable resistors for analog deep learning |url=http://li.mit.edu/Archive/Papers/22/Onen22EmondScience.pdf |journal=Science |language=en |volume=377 |issue=6605 |pages=539–543 |bibcode=2022Sci...377..539O |doi=10.1126/science.abp8064 |issn=0036-8075 |pmid=35901152 |s2cid=251159631}}</ref> In 2019, the European Union funded neuromorphic quantum computing to explore quantum operations using neuromorphic systems.<ref>{{Cite journal |title=Neuromorphic Quantum Computing {{!}} Quromorphic Project {{!}} Fact Sheet {{!}} H2020 |url=https://cordis.europa.eu/project/id/828826 |language=en |doi=10.3030/828826 |url-access=subscription |access-date=2024-03-18 |website=CORDIS {{!}} European Commission}}</ref> Also in 2022, researchers at the Max Planck Institute for Polymer Research developed an organic artificial spiking neuron for in-situ neuromorphic sensing and biointerfacing.<ref>{{cite journal |last1=Sarkar |first1=Tanmoy |last2=Lieberth |first2=Katharina |last3=Pavlou |first3=Aristea |last4=Frank |first4=Thomas |last5=Mailaender |first5=Volker |last6=McCulloch |first6=Iain |last7=Blom |first7=Paul W. M. |last8=Torriccelli |first8=Fabrizio |last9=Gkoupidenis |first9=Paschalis |date=7 November 2022 |title=An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing |journal=Nature Electronics |language=en |volume=5 |issue=11 |pages=774–783 |doi=10.1038/s41928-022-00859-y |hdl=10754/686016 |issn=2520-1131 |s2cid=253413801 |doi-access=free |hdl-access=free}}</ref>

Researchers reported in 2024 that chemical systems in liquid solutions can detect sound at various wavelengths, offering potential for neuromorphic applications.<ref>{{Cite journal |last1=Tomassoli |first1=Laura |last2=Silva-Dias |first2=Leonardo |last3=Dolnik |first3=Milos |last4=Epstein |first4=Irving R. |last5=Germani |first5=Raimondo |last6=Gentili |first6=Pier Luigi |date=2024-02-08 |title=Neuromorphic Engineering in Wetware: Discriminating Acoustic Frequencies through Their Effects on Chemical Waves |url=https://pubs.acs.org/doi/10.1021/acs.jpcb.3c08429 |journal=The Journal of Physical Chemistry B |language=en |volume=128 |issue=5 |pages=1241–1255 |doi=10.1021/acs.jpcb.3c08429 |issn=1520-6106 |pmid=38285636 |url-access=subscription}}</ref>

== Neurological inspiration == Neuromorphic engineering emulates the brain’s structure and operations, focusing on the analog nature of biological computation and the role of neurons in cognition. The brain processes information via neurons using chemical signals, abstracted into mathematical functions. Neuromorphic systems distribute computation across small elements, similar to neurons, using methods guided by anatomical and functional neural maps from electron microscopy and neural connection studies.<ref>{{cite journal |last1=Devineni |first1=Anita |title=A complete map of the fruit-fly |journal=Nature |date=2 October 2024 |volume=634 |issue=8032 |pages=35–36 |doi=10.1038/d41586-024-03029-6|pmid=39358530 }}</ref><ref>{{cite journal |last1=Wang |first1=Jun |last2=Jung |first2=Woo-Bin |last3=Gertner |first3=Rona |last4=Park |first4=Hongkun |last5=Ham |first5=Donhee |title=Synaptic connectivity mapping among thousands of neurons via parallelized intracellular recording with a microhole electrode array |journal=Nature Biomedical Engineering |date=2025 |volume=9 |issue=7 |pages=1144–1154 |doi=10.1038/s41551-025-01352-5 |pmid=39934437 |url=https://www.nature.com/articles/s41551-025-01352-5|url-access=subscription }}</ref> == Implementation == Neuromorphic systems employ hardware such as oxide-based memristors, spintronic memories, threshold switches, and transistors.<ref>{{Cite journal|last1=Maan|first1=A. K.|last2=Jayadevi|first2=D. A.|last3=James|first3=A. P.|date=2016-01-01|title=A Survey of Memristive Threshold Logic Circuits|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=PP|issue=99|pages=1734–1746|doi=10.1109/TNNLS.2016.2547842|pmid=27164608|issn=2162-237X|arxiv=1604.07121|bibcode=2016arXiv160407121M|s2cid=1798273}}</ref><ref>{{Cite journal|title = Mott Memory and Neuromorphic Devices|journal = Proceedings of the IEEE|date = 2015-08-01|issn = 0018-9219|pages = 1289–1310|volume = 103|issue = 8|doi = 10.1109/JPROC.2015.2431914|first1 = You|last1 = Zhou|first2 = S.|last2 = Ramanathan|s2cid = 11347598|url=https://zenodo.org/record/895565}}</ref> Software implementations train spiking neural networks using error backpropagation.<ref>{{cite arXiv|last1=Eshraghian|first1=Jason K.|last2=Ward|first2=Max|last3=Neftci |first3=Emre|last4=Wang|first4=Xinxin|last5=Lenz|first5=Gregor|last6=Dwivedi|first6=Girish|last7=Bennamoun|first7=Mohammed|last8=Jeong|first8=Doo Seok|last9=Lu|first9=Wei D.|title=Training Spiking Neural Networks Using Lessons from Deep Learning |date=1 October 2021 |class=cs.NE |eprint=2109.12894 }}</ref><ref>{{Cite web |url=https://github.com/Hananel-Hazan/bindsnet | title=Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch.| website=GitHub| date=31 March 2020}}</ref>

=== Neuromemristive systems === Neuromemristive systems use memristors to implement neuroplasticity, focusing on abstract neural network models rather than detailed biological mimicry.<ref>{{Cite web |url=https://digitalops.sandia.gov/Mediasite/Play/a10cf6ceb55d47608bb8326dd00e46611d |title=002.08 N.I.C.E. Workshop 2014: Towards Intelligent Computing with Neuromemristive Circuits and Systems – Feb. 2014 |website=digitalops.sandia.gov |access-date=2019-08-26}}</ref> These systems enable applications in speech recognition, face recognition, and object recognition, and can replace conventional digital logic gates.<ref>{{Cite journal|title = Memristor pattern recogniser: isolated speech word recognition|journal = Electronics Letters|pages = 1370–1372|volume = 51|issue = 17|doi = 10.1049/el.2015.1428|first1 = A.K.|last1 = Maan|first2 = A.P.|last2 = James|first3 = S.|last3 = Dimitrijev|year = 2015|bibcode = 2015ElL....51.1370M|hdl = 10072/140989|s2cid = 61454815|hdl-access = free}}</ref> The Caravelli-Traversa-Di Ventra equation describes memristive memory evolution, revealing tunneling phenomena and Lyapunov functions.<ref>{{cite journal |last=Caravelli |display-authors=etal|arxiv=1608.08651 |title=The complex dynamics of memristive circuits: analytical results and universal slow relaxation |year=2017 |doi=10.1103/PhysRevE.95.022140 |pmid= 28297937 |volume=95 |issue= 2 |article-number= 022140 |journal=Physical Review E|bibcode=2017PhRvE..95b2140C |s2cid=6758362}}</ref>

=== Neuromorphic sensors === Neuromorphic principles extend to sensors, such as the retinomorphic sensor or event camera, which mimic human vision by registering brightness changes individually, optimizing power consumption.<ref>{{Cite journal |last=Skorka |first=Orit |date=2011-07-01 |title=Toward a digital camera to rival the human eye |url=https://electronicimaging.spiedigitallibrary.org/article.aspx?doi=10.1117/1.3611015 |journal=Journal of Electronic Imaging |language=en |volume=20 |issue=3 |pages=033009–033009–18 |doi=10.1117/1.3611015 |bibcode=2011JEI....20c3009S |issn=1017-9909 |url-access=subscription}}</ref>

An example of this applied to detecting light is the retinomorphic sensor or, when employed in an array, an event camera.

== Ethical considerations == Neuromorphic systems raise the same ethical questions as those for other approaches to artificial intelligence. Daniel Lim argued that advanced neuromorphic systems could lead to machine consciousness, raising concerns about whether civil rights and other protocols should be extended to them.<ref>{{Cite journal|last=Lim|first=Daniel|date=2014-06-01|title=Brain simulation and personhood: a concern with the Human Brain Project|journal=Ethics and Information Technology|language=en|volume=16|issue=2|pages=77–89|doi=10.1007/s10676-013-9330-5|s2cid=17415814|issn=1572-8439}}</ref> Legal debates, such as in ''Acohs Pty Ltd v. Ucorp Pty Ltd'', question ownership of work produced by neuromorphic systems, as non-human-generated outputs may not be copyrightable.<ref>{{Cite web|url=http://www.lavan.com.au/advice/intellectual_property_technology/copyright_in_source_code_and_digital_products|title=Copyright in source code and digital products|last=Lavan|website=Lavan|language=en|access-date=2019-05-10}}</ref> == See also == {{Columns-list|colwidth=18em| * AI accelerator * Artificial brain * Biomorphic * Cognitive computer * Computation and Neural Systems * Differentiable programming * Event camera * Hardware for artificial intelligence * Lithionics * Neuromorphic olfaction systems * Neurorobotics * Optical flow sensor * Physical neural network * SpiNNaker * SyNAPSE * Retinomorphic sensor * Unconventional computing * Vision chip * Vision processing unit * Wetware computer * Zeroth (software)

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==References== {{Reflist|40em}}

==External links== {{Commons category|Neuromorphic engineering}} *[https://web.archive.org/web/20150727034331/http://ine-web.org/workshops/workshops-overview Telluride Neuromorphic Engineering Workshop] *[https://archive.today/20130115190057/http://capocaccia.ethz.ch/ CapoCaccia Cognitive Neuromorphic Engineering Workshop] *[https://web.archive.org/web/20190716132350/http://www.ine-web.org/ Institute of Neuromorphic Engineering] *[https://www.ine-news.org/ INE news site]. *[http://www.frontiersin.org/neuromorphic_engineering Frontiers in Neuromorphic Engineering Journal] *[http://www.cns.caltech.edu/ Computation and Neural Systems] department at the California Institute of Technology. *[https://www.the-scientist.com/features/building-a-silicon-brain-65738 Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models] {{Neuroscience}} {{Differentiable computing}} {{Authority control}}

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