neuromorphic computing

Neuromorphic engineering is for now set apart by the inspiration it takes from what is known about the structure and operations of the brain. Neuromorphic engineering translates what we know about the brain's function into computer systems. Work has mostly focused on replicating the analog nature of biological computation and the role of neurons in cognition.{{citation needed|date=February 2025}}

The biological processes of neurons and their synapses are dauntingly complex, and thus very difficult to artificially simulate. A key feature of biological brains is that all of the processing in neurons uses analog chemical signals. This makes it hard to replicate brains in computers because the current generation of computers is completely digital. However, the characteristics of these chemical signals can be abstracted into mathematical functions that closely capture the essence of the neuron's operations.{{citation needed|date=February 2025}}

The goal of neuromorphic computing is not to perfectly mimic the brain and all of its functions, but instead to extract what is known of its structure and operations to be used in a practical computing system. No neuromorphic system will claim nor attempt to reproduce every element of neurons and synapses, but all adhere to the idea that computation is highly distributed throughout a series of small computing elements analogous to a neuron. While this sentiment is standard, researchers chase this goal with different methods.{{Cite journal | doi = 10.1088/1741-2560/13/5/051001| title = Large-scale neuromorphic computing systems| journal = Journal of Neural Engineering| volume = 13| pages = 1–15| year = 2016| last1 = Furber | first1 = Steve| issue = 5| pmid = 27529195| bibcode = 2016JNEng..13e1001F| doi-access = free}} Anatomical neural wiring diagrams that are being imaged by electron microscopy{{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 }} and functional neural connection maps that could be potentially obtained via intracellular recording at scale{{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 |doi=10.1038/s41551-025-01352-5 |pmid=39934437 |url=https://www.nature.com/articles/s41551-025-01352-5}} can be used to better inspire, if not exactly mimicked, neuromorphic computing systems with more details.

The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,{{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}} spintronic memories, threshold switches, transistors,{{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}}{{Cite conference|author=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}} among others. The implementation details overlap with the concepts of Artificial Immune Systems. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g. using Python-based frameworks such as snnTorch,{{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 }} or using canonical learning rules from the biological learning literature, e.g. using BindsNet.{{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}}

{{Very long section|date=September 2022}}

As early as 2006, researchers at Georgia Tech published a field programmable neural array.{{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}} This chip was the first in a line of increasingly complex arrays of floating gate transistors that allowed programmability of charge on the gates of MOSFETs to model the channel-ion characteristics of neurons in the brain and was one of the first cases of a silicon programmable array of neurons.

In November 2011, a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using 400 transistors and standard CMOS manufacturing techniques.{{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}}{{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}}

In June 2012, spintronic researchers at Purdue University presented a paper on the design of a neuromorphic chip using lateral spin valves and memristors. They argue that the architecture works similarly to neurons and can therefore be used to test methods of reproducing the brain's processing. In addition, these chips are significantly more energy-efficient than conventional ones.{{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}}

Research at HP Labs on Mott memristors has shown that while they can be non-volatile, the volatile behavior exhibited at temperatures significantly below the phase transition temperature can be exploited to fabricate a neuristor, a biologically-inspired device that mimics behavior found in neurons.{{Cite journal | doi = 10.1038/nmat3510| pmid = 23241533| title = A scalable neuristor built with Mott memristors| journal = Nature Materials| volume = 12| issue = 2| pages = 114–7| year = 2012| last1 = Pickett | first1 = M. D. | last2 = Medeiros-Ribeiro | first2 = G. | last3 = Williams | first3 = R. S. | bibcode = 2013NatMa..12..114P| s2cid = 16271627}} In September 2013, they presented models and simulations that show how the spiking behavior of these neuristors can be used to form the components required for a Turing machine.{{cite journal|doi=10.1088/0957-4484/24/38/384002|title=Phase transitions enable computational universality in neuristor-based cellular automata|author1=Matthew D Pickett|author2=R Stanley Williams|name-list-style=amp|date=September 2013|publisher=IOP Publishing Ltd|journal=Nanotechnology|volume=24|issue=38|pmid=23999059|bibcode=2013Nanot..24L4002P|s2cid=9910142 |at=384002}}

Neurogrid, built by Brains in Silicon at Stanford University,{{cite journal|last1=Boahen|first1=Kwabena|title=Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations|journal=Proceedings of the IEEE|date=24 April 2014|volume=102|issue=5|pages=699–716|doi=10.1109/JPROC.2014.2313565|s2cid=17176371}} is an example of hardware designed using neuromorphic engineering principles. The circuit board is composed of 16 custom-designed chips, referred to as NeuroCores. Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency. The emulated neurons are connected using digital circuitry designed to maximize spiking throughput.{{cite journal|doi=10.1038/503022a|pmid = 24201264|title = Neuroelectronics: Smart connections|journal = Nature|volume = 503|issue = 7474|pages = 22–4|year = 2013|last1 = Waldrop|first1 = M. Mitchell|bibcode = 2013Natur.503...22W|doi-access = free}}{{cite journal|doi=10.1109/JPROC.2014.2313565|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|year = 2014|last1 = Benjamin|first1 = Ben Varkey|last2 = Peiran Gao|last3 = McQuinn|first3 = Emmett|last4 = Choudhary|first4 = Swadesh|last5 = Chandrasekaran|first5 = Anand R.|last6 = Bussat|first6 = Jean-Marie|last7 = Alvarez-Icaza|first7 = Rodrigo|last8 = Arthur|first8 = John V.|last9 = Merolla|first9 = Paul A.|last10 = Boahen|first10 = Kwabena|s2cid = 17176371}}

A research project with implications for neuromorphic engineering is the Human Brain Project that is attempting to simulate a complete human brain in a supercomputer using biological data. It is made up of a group of researchers in neuroscience, medicine, and computing.{{cite web|title=Involved Organizations|url=http://www.humanbrainproject.eu/partners.html|access-date=22 February 2013|archive-url=https://web.archive.org/web/20130302142627/http://www.humanbrainproject.eu/partners.html|archive-date=2 March 2013}} Henry Markram, the project's co-director, has stated that the project proposes to establish a foundation to explore and understand the brain and its diseases, and to use that knowledge to build new computing technologies. The three primary goals of the project are to better understand how the pieces of the brain fit and work together, to understand how to objectively diagnose and treat brain diseases and to use the understanding of the human brain to develop neuromorphic computers. Since the simulation of a complete human brain will require a powerful supercomputer, the current focus on neuromorphic computers is being encouraged.{{cite web|title=Human Brain Project|url=http://www.humanbrainproject.eu|access-date=22 February 2013}} $1.3{{Nbsp}}billion has been allocated to the project by The European Commission.{{cite web|title=The Human Brain Project and Recruiting More Cyberwarriors|url=http://www.marketplace.org/topics/tech/human-brain-project-and-recruiting-more-cyberwarriors|access-date=22 February 2013|date=January 29, 2013}}

Other research with implications for neuromorphic engineering involve the BRAIN Initiative[https://www.economist.com/news/science-and-technology/21582495-computers-will-help-people-understand-brains-better-and-understanding-brains Neuromorphic computing: The machine of a new soul], The Economist, 2013-08-03 and the TrueNorth chip from IBM.{{cite journal|last1=Modha|first1=Dharmendra|title=A million spiking-neuron integrated circuit with a scalable communication network and interface|journal=Science|date=Aug 2014|volume=345|issue=6197|pages=668–673|doi=10.1126/science.1254642|pmid=25104385|bibcode=2014Sci...345..668M|s2cid=12706847}} Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.{{Cite web|url=http://jessamynfairfield.com/wp-content/uploads/2017/03/PWMar17Fairfield.pdf|title=Smarter Machines|last=Fairfield|first=Jessamyn|date=March 1, 2017}} There also is development of a memristive device for quantum neuromorphic architectures.{{cite journal |last1=Spagnolo |first1=Michele |last2=Morris |first2=Joshua |last3=Piacentini |first3=Simone |last4=Antesberger |first4=Michael |last5=Massa |first5=Francesco |last6=Crespi |first6=Andrea |last7=Ceccarelli |first7=Francesco |last8=Osellame |first8=Roberto |last9=Walther |first9=Philip |title=Experimental photonic quantum memristor |journal=Nature Photonics |date=April 2022 |volume=16 |issue=4 |pages=318–323 |doi=10.1038/s41566-022-00973-5 |arxiv=2105.04867 |bibcode=2022NaPho..16..318S |s2cid=234358015 |language=en |issn=1749-4893}}
News article: {{cite news |title=Erster "Quanten-Memristor" soll KI und Quantencomputer verbinden |url=https://www.derstandard.de/consent/tcf/story/2000134458057/erster-quanten-memristor-sollki-und-quantencomputer-verbinden |access-date=28 April 2022 |work=DER STANDARD |language=de-AT}}
Lay summary report: {{cite news |title=Artificial neurons go quantum with photonic circuits |url=https://phys.org/news/2022-03-artificial-neurons-quantum-photonic-circuits.html |access-date=19 April 2022 |work=University of Vienna |language=en}} In 2022, researchers at MIT have reported the development of brain-inspired artificial synapses, using the ion proton ({{chem|H|+}}), for 'analog deep learning'.{{cite news |title='Artificial synapse' could make neural networks work more like brains |url=https://www.newscientist.com/article/2331368-artificial-synapse-could-make-neural-networks-work-more-like-brains/ |access-date=21 August 2022 |work=New Scientist}}{{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. |title=Nanosecond protonic programmable resistors for analog deep learning |journal=Science |date=29 July 2022 |volume=377 |issue=6605 |pages=539–543 |doi=10.1126/science.abp8064 |pmid=35901152 |bibcode=2022Sci...377..539O |s2cid=251159631 |url=http://li.mit.edu/Archive/Papers/22/Onen22EmondScience.pdf |language=en |issn=0036-8075}}

Intel unveiled its neuromorphic research chip, called "Loihi", in October 2017. The chip uses an asynchronous spiking neural network (SNN) to implement adaptive self-modifying event-driven fine-grained parallel computations used to implement learning and inference with high efficiency.{{cite journal |last1=Davies |first1=Mike |title=Loihi: A Neuromorphic Manycore Processor with On-Chip Learning |journal=IEEE Micro |date=January 16, 2018 |volume=38 |issue=1 |pages=82–99 |display-authors=etal|doi=10.1109/MM.2018.112130359 |s2cid=3608458 }}{{cite web |last1=Morris |first1=John |title=Why Intel built a neuromorphic chip |url=https://www.zdnet.com/article/why-intel-built-a-neuromorphic-chip/ |website=ZDNet |access-date=17 August 2018 |language=en}}

IMEC, a Belgium-based nanoelectronics research center, demonstrated the world's first self-learning neuromorphic chip. The brain-inspired chip, based on OxRAM technology, has the capability of self-learning and has been demonstrated to have the ability to compose music.{{cite web |title=Imec demonstrates self-learning neuromorphic chip that composes music |url=https://www.imec-int.com/en/articles/imec-demonstrates-self-learning-neuromorphic-chip-that-composes-music |website=IMEC International |access-date=1 October 2019}} IMEC released the 30-second tune composed by the prototype. The chip was sequentially loaded with songs in the same time signature and style. The songs were old Belgian and French flute minuets, from which the chip learned the rules at play and then applied them.{{cite web|last1=Bourzac|first1=Katherine|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|date=May 23, 2017}}

The Blue Brain Project, led by Henry Markram, aims to build biologically detailed digital reconstructions and simulations of the mouse brain. The Blue Brain Project has created in silico models of rodent brains, while attempting to replicate as many details about its biology as possible. The supercomputer-based simulations offer new perspectives on understanding the structure and functions of the brain.

The European Union funded a series of projects at the University of Heidelberg, which led to the development of BrainScaleS (brain-inspired multiscale computation in neuromorphic hybrid systems), a hybrid analog neuromorphic supercomputer located at Heidelberg University, Germany. It was developed as part of the Human Brain Project neuromorphic computing platform and is the complement to the SpiNNaker supercomputer (which is based on digital technology). The architecture used in BrainScaleS mimics biological neurons and their connections on a physical level; additionally, since the components are made of silicon, these model neurons operate on average 864 times (24 hours of real time is 100 seconds in the machine simulation) faster than that of their biological counterparts.{{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}}

In 2019, the European Union funded the project "Neuromorphic quantum computing"{{Cite journal |title=Neuromrophic Quantum Computing {{!}} Quromorphic Project {{!}} Fact Sheet {{!}} H2020 |url=https://cordis.europa.eu/project/id/828826 |access-date=2024-03-18 |website=CORDIS {{!}} European Commission |language=en |doi=10.3030/828826}} exploring the use of neuromorphic computing to perform quantum operations. Neuromorphic quantum computing{{Citation |last1=Pehle |first1=Christian |title=Neuromorphic quantum computing |date=2021-03-30 |arxiv=2005.01533 |last2=Wetterich |first2=Christof|journal=Physical Review E |volume=106 |issue=4 |page=045311 |doi=10.1103/PhysRevE.106.045311 |pmid=36397478 |bibcode=2022PhRvE.106d5311P }} (abbreviated as 'n.quantum computing') is an unconventional computing type of computing that uses neuromorphic computing to perform quantum operations.{{Cite journal |last=Wetterich |first=C. |date=2019-11-01 |title=Quantum computing with classical bits |url=https://www.sciencedirect.com/science/article/pii/S0550321319302627 |journal=Nuclear Physics B |volume=948 |page=114776 |doi=10.1016/j.nuclphysb.2019.114776 |issn=0550-3213|arxiv=1806.05960 |bibcode=2019NuPhB.94814776W }}{{Citation |last1=Pehle |first1=Christian |title=Emulating quantum computation with artificial neural networks |date=2018-10-24 |arxiv=1810.10335 |last2=Meier |first2=Karlheinz |last3=Oberthaler |first3=Markus |last4=Wetterich |first4=Christof}} It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with neuromorphic quantum computing.{{Cite journal |last1=Carleo |first1=Giuseppe |last2=Troyer |first2=Matthias |date=2017-02-10 |title=Solving the quantum many-body problem with artificial neural networks |url=https://www.science.org/doi/10.1126/science.aag2302 |journal=Science |language=en |volume=355 |issue=6325 |pages=602–606 |doi=10.1126/science.aag2302 |pmid=28183973 |issn=0036-8075|arxiv=1606.02318 |bibcode=2017Sci...355..602C }}{{Cite journal |last1=Torlai |first1=Giacomo |last2=Mazzola |first2=Guglielmo |last3=Carrasquilla |first3=Juan |last4=Troyer |first4=Matthias |last5=Melko |first5=Roger |last6=Carleo |first6=Giuseppe |date=May 2018 |title=Neural-network quantum state tomography |url=https://www.nature.com/articles/s41567-018-0048-5 |journal=Nature Physics |language=en |volume=14 |issue=5 |pages=447–450 |doi=10.1038/s41567-018-0048-5 |issn=1745-2481|arxiv=1703.05334 |bibcode=2018NatPh..14..447T }}{{Cite journal |last1=Sharir |first1=Or |last2=Levine |first2=Yoav |last3=Wies |first3=Noam |last4=Carleo |first4=Giuseppe |last5=Shashua |first5=Amnon |date=2020-01-16 |title=Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems |url=https://link.aps.org/doi/10.1103/PhysRevLett.124.020503 |journal=Physical Review Letters |volume=124 |issue=2 |page=020503 |doi=10.1103/PhysRevLett.124.020503|pmid=32004039 |arxiv=1902.04057 |bibcode=2020PhRvL.124b0503S }}{{Citation |last1=Broughton |first1=Michael |title=TensorFlow Quantum: A Software Framework for Quantum Machine Learning |date=2021-08-26 |arxiv=2003.02989 |last2=Verdon |first2=Guillaume |last3=McCourt |first3=Trevor |last4=Martinez |first4=Antonio J. |last5=Yoo |first5=Jae Hyeon |last6=Isakov |first6=Sergei V. |last7=Massey |first7=Philip |last8=Halavati |first8=Ramin |last9=Niu |first9=Murphy Yuezhen}}{{Citation |last=Di Ventra |first=Massimiliano |title=MemComputing vs. Quantum Computing: some analogies and major differences |date=2022-03-23 |arxiv=2203.12031}} Both, traditional quantum computing and neuromorphic quantum computing are physics-based unconventional computing approaches to computations and do not follow the von Neumann architecture. They both construct a system (a circuit) that represents the physical problem at hand, and then leverage their respective physics properties of the system to seek the "minimum". Neuromorphic quantum computing and quantum computing share similar physical properties during computation.{{Cite journal |last1=Wilkinson |first1=Samuel A. |last2=Hartmann |first2=Michael J. |date=2020-06-08 |title=Superconducting quantum many-body circuits for quantum simulation and computing |journal=Applied Physics Letters |volume=116 |issue=23 |doi=10.1063/5.0008202 |issn=0003-6951|arxiv=2003.08838 |bibcode=2020ApPhL.116w0501W }}

Brainchip announced in October 2021 that it was taking orders for its Akida AI Processor Development Kits{{Cite web|url=https://cdn-api.markitdigital.com/apiman-gateway/ASX/asx-research/1.0/file/2924-02438858-2A1332482?access_token=83ff96335c2d45a094df02a206a39ff4|title=Taking Orders of Akida AI Processor Development Kits|date=21 October 2021}} and in January 2022 that it was taking orders for its Akida AI Processor PCIe boards,{{cite web | url=https://www.electronics-lab.com/first-mini-pciexpress-board-with-spiking-neural-network-chip/ | title=First mini PCIexpress board with spiking neural network chip | date=January 19, 2022 }} making it the world's first commercially available neuromorphic processor.

Neuromemristive systems are a subclass of neuromorphic computing systems that focuses on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction.{{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}} For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.C. Merkel and D. Kudithipudi, "Neuromemristive extreme learning machines for pattern classification," ISVLSI, 2014.

There exist several neuron inspired threshold logic functions implemented with memristors that have applications in high level pattern recognition applications. Some of the applications reported recently include speech recognition,{{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}} face recognition{{Cite journal|title = Memristive Threshold Logic Face Recognition|journal = Procedia Computer Science|date = 2014-01-01|pages = 98–103|volume = 41|series = 5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA|doi = 10.1016/j.procs.2014.11.090|first1 = Akshay Kumar|last1 = Maan|first2 = Dinesh S.|last2 = Kumar|first3 = Alex Pappachen|last3 = James|doi-access = free|hdl = 10072/68372|hdl-access = free}} and object recognition.{{Cite journal|title = Memristive Threshold Logic Circuit Design of Fast Moving Object Detection|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2015-10-01|issn = 1063-8210|pages = 2337–2341|volume = 23|issue = 10|doi = 10.1109/TVLSI.2014.2359801|first1 = A.K.|last1 = Maan|first2 = D.S.|last2 = Kumar|first3 = S.|last3 = Sugathan|first4 = A.P.|last4 = James|arxiv = 1410.1267|s2cid = 9647290}} They also find applications in replacing conventional digital logic gates.{{Cite journal|title = Resistive Threshold Logic|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2014-01-01|issn = 1063-8210|pages = 190–195|volume = 22|issue = 1|doi = 10.1109/TVLSI.2012.2232946|first1 = A.P.|last1 = James|first2 = L.R.V.J.|last2 = Francis|first3 = D.S.|last3 = Kumar|arxiv = 1308.0090|s2cid = 7357110}}{{Cite journal|title = Threshold Logic Computing: Memristive-CMOS Circuits for Fast Fourier Transform and Vedic Multiplication|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2015-11-01|issn = 1063-8210|pages = 2690–2694|volume = 23|issue = 11|doi = 10.1109/TVLSI.2014.2371857|first1 = A.P.|last1 = James|first2 = D.S.|last2 = Kumar|first3 = A.|last3 = Ajayan|arxiv = 1411.5255|s2cid = 6076956}}

For (quasi)ideal passive memristive circuits, the evolution of the memristive memories can be written in a closed form (Caravelli–Traversa–Di Ventra equation):{{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 |page= 022140 |journal=Physical Review E|bibcode=2017PhRvE..95b2140C |s2cid=6758362}}{{cite journal |last=Caravelli |display-authors=etal|arxiv=1608.08651 |title=Global minimization via classical tunneling assisted by collective force field formation |year=2021 |doi=10.1126/sciadv.abh1542 |pmid= 28297937 |volume=7 |issue=52 |journal=Science Advances|page=022140 |bibcode=2021SciA....7.1542C |s2cid=231847346 }}

:$\backslash frac\{d\}\{dt\}\; \backslash vec\{X\}\; =\; -\backslash alpha\; \backslash vec\{X\}+\backslash frac\{1\}\{\backslash beta\}\; (I-\backslash chi\; \backslash Omega\; X)^\{-1\}\; \backslash Omega\; \backslash vec\; S$

as a function of the properties of the physical memristive network and the external sources. The equation is valid for the case of the Williams-Strukov original toy model, as in the case of ideal memristors, $\backslash alpha=0$. However, the hypothesis of the existence of an ideal memristor is debatable.{{Cite journal |last=Abraham |first=Isaac |date=2018-07-20 |title=The case for rejecting the memristor as a fundamental circuit element |journal=Scientific Reports |language=en |volume=8 |issue=1 |page=10972 |doi=10.1038/s41598-018-29394-7 |issn=2045-2322 |pmc=6054652 |pmid=30030498|bibcode=2018NatSR...810972A }} In the equation above, $\backslash alpha$ is the "forgetting" time scale constant, typically associated to memory volatility, while $\backslash chi=\backslash frac\{R\_\backslash text\{off\}-R\_\backslash text\{on\}\}\{R\_\backslash text\{off\}\}$ is the ratio of off and on values of the limit resistances of the memristors, $\backslash vec\; S$ is the vector of the sources of the circuit and $\backslash Omega$ is a projector on the fundamental loops of the circuit. The constant $\backslash beta$ has the dimension of a voltage and is associated to the properties of the memristor; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector $X=\backslash operatorname\{diag\}(\backslash vec\; X)$ and $\backslash vec\; X$ respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.

It has been recently shown that the equation above exhibits tunneling phenomena and used to study Lyapunov functions.{{Cite book |last=Sheldon |first=Forrest |title=Collective Phenomena in Memristive Networks: Engineering phase transitions into computation |publisher=UC San Diego Electronic Theses and Dissertations |year=2018}}

The concept of neuromorphic systems can be extended to sensors (not just to computation). An example of this applied to detecting light is the retinomorphic sensor or, when employed in an array, the event camera. An event camera's pixels all register changes in brightness levels individually, which makes these cameras comparable to human eyesight in their theoretical power consumption.{{Cite journal |last=Skorka |first=Orit |date=2011-07-01 |title=Toward a digital camera to rival the human eye |url=http://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}} In 2022, researchers from the Max Planck Institute for Polymer Research reported an organic artificial spiking neuron that exhibits the signal diversity of biological neurons while operating in the biological wetware, thus enabling in-situ neuromorphic sensing and biointerfacing applications.{{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 |title=An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing |journal=Nature Electronics |date=7 November 2022 |volume=5 |issue=11 |pages=774–783 |doi=10.1038/s41928-022-00859-y |s2cid=253413801 |language=en |issn=2520-1131|doi-access=free |hdl=10754/686016 |hdl-access=free }}{{cite journal |title=Artificial neurons emulate biological counterparts to enable synergetic operation |journal=Nature Electronics |date=10 November 2022 |volume=5 |issue=11 |pages=721–722 |doi=10.1038/s41928-022-00862-3 |s2cid=253469402 |url=https://www.nature.com/articles/s41928-022-00862-3 |language=en |issn=2520-1131}}

{{Unreferenced section|date=January 2023}}

The Joint Artificial Intelligence Center, a branch of the U.S. military, is a center dedicated to the procurement and implementation of AI software and neuromorphic hardware for combat use. Specific applications include smart headsets/goggles and robots. JAIC intends to rely heavily on neuromorphic technology to connect "every sensor (to) every shooter" within a network of neuromorphic-enabled units.

While the interdisciplinary concept of neuromorphic engineering is relatively new, many of the same ethical considerations apply to neuromorphic systems as apply to human-like machines and artificial intelligence in general. However, the fact that neuromorphic systems are designed to mimic a human brain gives rise to unique ethical questions surrounding their usage.

However, the practical debate is that neuromorphic hardware as well as artificial "neural networks" are immensely simplified models of how the brain operates or processes information at a much lower complexity in terms of size and functional technology and a much more regular structure in terms of connectivity. Comparing neuromorphic chips to the brain is a very crude comparison similar to comparing a plane to a bird just because they both have wings and a tail. The fact is that biological neural cognitive systems are many orders of magnitude more energy- and compute-efficient than current state-of-the-art AI and neuromorphic engineering is an attempt to narrow this gap by inspiring from the brain's mechanism just like many engineering designs have bio-inspired features.

Significant ethical limitations may be placed on neuromorphic engineering due to public perception.{{Cite report|url=https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai_100_report_0831fnl.pdf|title=Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence |date=September 2016 |publisher=Stanford University|access-date=December 26, 2019|archive-date=May 30, 2019|archive-url=https://web.archive.org/web/20190530152437/https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai_100_report_0831fnl.pdf}} Special Eurobarometer 382: Public Attitudes Towards Robots, a survey conducted by the European Commission, found that 60% of European Union citizens wanted a ban of robots in the care of children, the elderly, or the disabled. Furthermore, 34% were in favor of a ban on robots in education, 27% in healthcare, and 20% in leisure. The European Commission classifies these areas as notably "human." The report cites increased public concern with robots that are able to mimic or replicate human functions. Neuromorphic engineering, by definition, is designed to replicate the function of the human brain.{{Cite web|url=http://ec.europa.eu/commfrontoffice/publicopinion/archives/ebs/ebs_382_en.pdf|title=Special Eurobarometer 382: Public Attitudes Towards Robots|last=European Commission|date=September 2012|website=European Commission}}

The social concerns surrounding neuromorphic engineering are likely to become even more profound in the future. The European Commission found that EU citizens between the ages of 15 and 24 are more likely to think of robots as human-like (as opposed to instrument-like) than EU citizens over the age of 55. When presented an image of a robot that had been defined as human-like, 75% of EU citizens aged 15–24 said it corresponded with the idea they had of robots while only 57% of EU citizens over the age of 55 responded the same way. The human-like nature of neuromorphic systems, therefore, could place them in the categories of robots many EU citizens would like to see banned in the future.

As neuromorphic systems have become increasingly advanced, some scholars{{who|date=August 2021}} have advocated for granting personhood rights to these systems. Daniel Lim, a critic of technology development in the Human Brain Project, which aims to advance brain-inspired computing, has argued that advancement in neuromorphic computing could lead to machine consciousness or personhood.{{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}} If these systems are to be treated as people, then many tasks humans perform using neuromorphic systems, including their termination, may be morally impermissible as these acts would violate their autonomy.

There is significant legal debate around property rights and artificial intelligence. In Acohs Pty Ltd v. Ucorp Pty Ltd, Justice Christopher Jessup of the Federal Court of Australia found that the source code for Material Safety Data Sheets could not be copyrighted as it was generated by a software interface rather than a human author.{{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}} The same question may apply to neuromorphic systems: if a neuromorphic system successfully mimics a human brain and produces a piece of original work, who, if anyone, should be able to claim ownership of the work?{{cite journal |last1=Eshraghian|first1=Jason K. |title=Human Ownership of Artificial Creativity |journal=Nature Machine Intelligence |date=9 March 2020 |volume=2 |issue=3 |pages=157–160 |doi=10.1038/s42256-020-0161-x|s2cid=215991449 }}

{{Columns-list|colwidth=18em|

• AI accelerator
• Artificial brain
• Biomorphic
• Cognitive computer
• Computation and Neural Systems
• Differentiable programming
• Event camera
• Hardware for artificial intelligence
• Lithionics
• Neurorobotics
• Optical flow sensor
• Physical neural network
• SpiNNaker
• SyNAPSE
• Retinomorphic sensor
• Unconventional computing
• Vision chip
• Vision processing unit
• Wetware computer
• Zeroth (software)

}}

{{Portal bar|Electronics}}

{{Reflist|40em}}

{{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]
• [http://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}}

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