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第1行： − 此词条暂由彩云小译翻译，翻译字数共3，未经人工整理和审校，带来阅读不便，请见谅。+ − #REDIRECT [[Neuromorphic engineering]]+ + − #REDIRECT Neuromorphic engineering+ − # 神经形态工程学+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − +
Moved page from wikipedia:en:Neuromorphic engineering (history)
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{{Use American English|date = January 2019}}
{{Use mdy dates|date = January 2019}}
'''Neuromorphic engineering''', also known as '''neuromorphic computing''',<ref>{{Cite journal | doi = 10.1145/2601069| title = Neuromorphic computing gets ready for the (really) big time| journal = [[Communications of the ACM]]| volume = 57| issue = 6| year = 2014| last1 = Monroe | first1 = D. | pages = 13–15| s2cid = 20051102}}</ref><ref>{{Cite journal | doi = 10.1088/0957-4484/21/17/175202| pmid = 20368686| title = Nanotube devices based crossbar architecture: Toward neuromorphic computing| journal = Nanotechnology| volume = 21| issue = 17| pages = 175202| year = 2010| last1 = Zhao | first1 = W. S. | last2 = Agnus | first2 = G. | last3 = Derycke | first3 = V. | last4 = Filoramo | first4 = A. | last5 = Bourgoin | first5 = J. -P. | last6 = Gamrat | first6 = C. | bibcode = 2010Nanot..21q5202Z| url = https://zenodo.org/record/3428659}}</ref><ref name="humanbrainproject">{{YouTube|id=6RoiZ90mGfw|title=The Human Brain Project SP 9: Neuromorphic Computing Platform}}</ref> is the use of [[very-large-scale integration]] (VLSI) systems containing electronic [[analog circuit]]s to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations.<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|url=https://authors.library.caltech.edu/53090/1/00058356.pdf}}</ref><ref name=":2" /> 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 system]]s (for [[perception]], [[motor control]], or [[multisensory integration]]). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based [[memristor]]s,<ref name="Maan 1–13">{{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> spintronic memories, threshold switches, and [[transistor]]s.<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><ref name=":2">{{Cite document|title=Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling {{!}} International Conference on Neuromorphic Systems 2020|language=EN|doi=10.1145/3407197.3407204|s2cid=220794387}}</ref> Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using [[Python (programming language)|Python]] based frameworks such as snnTorch,<ref>{{cite journal |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 |arxiv=2109.12894 }}</ref> or using canonical learning rules from the biological learning literature, e.g., using BindsNet.<ref>{{Cite web | url=https://github.com/Hananel-Hazan/bindsnet | title=Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch.| date=31 March 2020}}</ref>
Neuromorphic engineering, also known as neuromorphic computing, is the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations. 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). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
神经形态工程，也称为神经形态计算，是使用包含电子模拟电路的超大规模集成电路系统来模拟神经系统中的神经生物结构。神经形态计算机/芯片是任何使用物理人造神经元(由硅制成)进行计算的设备。近年来，神经形态学这个术语被用来描述模拟、数字、混合模式模拟/数字 VLSI，以及实现神经系统模型的软件系统(感知、运动控制或多感官整合)。神经形态计算的硬件实现可以通过基于氧化物的记忆电阻、自旋电子存储器、阈值开关和晶体管来实现。训练基于软件的脉冲神经网络的神经形态系统可以通过错误反向传播来实现，例如，使用基于 Python 的框架，例如 snnTorch，或者使用来自生物学习文献的规范学习规则，例如，使用 BindsNet。
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.
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.
神经形态工程的一个关键方面是理解单个神经元、电路、应用和整体结构的形态如何产生理想的计算，如何影响信息的表示，如何影响对损伤的鲁棒性，如何整合学习和发展，如何适应局部变化(可塑性) ，以及促进进化变化。
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from [[biology]], [[physics]], [[mathematics]], [[computer science]], and [[electronic engineering]]<ref name=":2" /> to design artificial neural systems, such as [[Machine vision|vision systems]], head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.<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> It was developed by [[Carver Mead]]<ref>{{cite web|title=carver mead website|last1=Mead|first1=Carver|url=http://www.carvermead.caltech.edu/index.html|website=carvermead}}</ref> in the late 1980s.
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering 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. It was developed by Carver Mead in the late 1980s.
神经形态工程是以生物学、物理学、数学、计算机科学和电子工程为基础，设计人工神经系统，如视觉系统、头眼系统、听觉处理器和自主机器人的一门交叉学科。它是由卡弗 · 米德在20世纪80年代后期开发的。
==Neurological inspiration==
Neuromorphic engineering is set apart by the inspiration it takes from what we know 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 [[neuron]]s in [[cognition]].
Neuromorphic engineering is set apart by the inspiration it takes from what we know 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.
神经学灵感工程学是从我们对大脑结构和运作的了解中获得灵感而形成的。神经形态工程学将我们对大脑功能的了解转化为计算机系统。工作主要集中在复制生物计算的模拟特性和神经元在认知中的作用。
The biological processes of neurons and their [[synapse]]s are dauntingly complex, and thus very difficult to artificially simulate. A key feature of biological brains is that all of the processing in neurons use 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 parts can be abstracted into mathematical functions that closely capture the essence of the neuron's operations.
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 use 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 parts can be abstracted into mathematical functions that closely capture the essence of the neuron's operations.
神经元及其突触的生物学过程极其复杂，难以人工模拟。生物大脑的一个关键特征是，神经元的所有处理过程都使用模拟化学信号。这使得很难在计算机中复制大脑，因为目前的计算机是完全数字化的。然而，这些部分的特征可以抽象为数学函数，这些函数紧密地抓住了神经元运行的本质。
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 processing|distributed]] throughout a series of small computing elements analogous to a neuron. While this sentiment is standard, researchers chase this goal with different methods.<ref>{{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}}</ref>
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.
神经形态计算的目标不是完美地模拟大脑及其所有功能，而是提取已知的大脑结构和操作，用于实际的计算系统。没有哪个神经形态学系统会声称或试图复制神经元和突触的每一个元素，但所有人都坚持这样的观点，即计算是高度分布在一系列类似于神经元的小型计算元素中的。虽然这种情绪是标准的，但研究人员用不同的方法追求这一目标。
==Examples==
As early as 2006, researchers at [[Georgia Tech]] published a field programmable neural array.<ref>{{Cite book|title = A field programmable neural array|last1 = Farquhar|first1 = Ethan|date = May 2006|journal = IEEE International Symposium on Circuits and Systems|pages = 4114–4117|last2 = Hasler|first2 = Paul.|doi = 10.1109/ISCAS.2006.1693534|isbn = 978-0-7803-9389-9|s2cid = 206966013}}</ref> 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 [[MOSFET]]s 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.
As early as 2006, researchers at Georgia Tech published a field programmable neural array. 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.
早在2006年，佐治亚理工学院的研究人员就发表了现场可编程神经阵列。这种芯片是一系列越来越复杂的浮栅晶体管阵列中的第一个，这些晶体管可以在 mosfet 的栅极上编程来模拟大脑中神经元的通道离子特性，这也是硅可编程神经元阵列的首批案例之一。
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.<ref>{{cite web|title=MIT creates "brain chip"|url=http://www.extremetech.com/extreme/105067-mit-creates-brain-chip|access-date=4 December 2012}}</ref><ref name="Neuromorphic silicon paper">{{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|pages=108|journal=Frontiers in Neuroscience|year=2011|last1=Poon|first1=Chi-Sang|last2=Zhou|first2=Kuan|doi-access=free}}</ref>
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.
2011年11月，麻省理工学院的一组研究人员发明了一种计算机芯片，该芯片使用400个晶体管和标准的 CMOS 制造技术，在两个神经元之间的突触中模拟模拟基于离子的通讯。
In June 2012, [[spintronic]] researchers at [[Purdue University]] presented a paper on the design of a neuromorphic chip using [[Spin valve|lateral spin valve]]s and [[memristor]]s. 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.<ref name="Spin Devices Prop">{{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>
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.
2012年6月，普渡大学的自旋电子学研究人员发表了一篇关于利用侧向自旋阀和记忆电阻器设计神经形态芯片的论文。他们认为，这种结构的工作原理与神经元相似，因此可以用来测试复制大脑处理过程的方法。此外，这些芯片明显比传统芯片更节能。
Research at [[HP Labs]] on Mott memristors has shown that while they can be non-[[Volatile memory|volatile]], the volatile behavior exhibited at temperatures significantly below the [[phase transition]] temperature can be exploited to fabricate a [[neuristor]],<ref name=":0" /> a biologically-inspired device that mimics behavior found in neurons.<ref name=":0">{{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| url = https://semanticscholar.org/paper/b6ba6f496ace2b947f111059663e76bb60e9efeb}}</ref> 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]].<ref>{{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|at=384002}}</ref>
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. 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.
惠普实验室在莫特记忆电阻器上的研究表明，尽管它们可以是非挥发性的，但是在相变温度以下显示的挥发性行为可以被用来制造神经元电阻器，这是一种模仿神经元行为的生物设备。2013年9月，他们展示了模型和仿真，展示了这些神经元的尖峰行为是如何被用来形成图灵机所需的元件的。
[[Neurogrid]], built by ''Brains in Silicon'' at [[Stanford University]],<ref>{{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}}</ref> 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.<ref>{{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}}</ref><ref>{{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}}</ref>
Neurogrid, built by Brains in Silicon at Stanford University, 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.
神经网格是由斯坦福大学硅谷大脑公司建立的，是一个使用神经形态工程原理设计硬件的例子。该电路板由16个定制设计的芯片组成，称为 NeuroCores。每个 NeuroCore 的模拟电路被设计为模拟65536个神经元的神经元元件，最大限度地提高能量效率。模拟的神经元通过设计的数字电路连接，以最大化脉冲吞吐量。
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.<ref>{{cite web|title=Involved Organizations|url=http://www.humanbrainproject.eu/partners.html|access-date=22 February 2013|url-status=dead|archive-url=https://web.archive.org/web/20130302142627/http://www.humanbrainproject.eu/partners.html|archive-date=2 March 2013}}</ref> [[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. That the simulation of a complete human brain will require a supercomputer a thousand times more powerful than today's encourages the current focus on neuromorphic computers.<ref>{{cite web|title=Human Brain Project|url=http://www.humanbrainproject.eu|access-date=22 February 2013}}</ref> $1.3 billion has been allocated to the project by The [[European Commission]].<ref>{{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}}</ref>
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. 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. That the simulation of a complete human brain will require a supercomputer a thousand times more powerful than today's encourages the current focus on neuromorphic computers. $1.3 billion has been allocated to the project by The European Commission.
一个对神经形态工程有影响的研究项目是人脑项目，它试图用生物数据在超级计算机中模拟完整的人脑。它由一群神经科学、医学和计算机科学的研究人员组成。该项目的联合主管亨利•马克拉姆(Henry Markram)表示，该项目提议建立一个基础，以探索和了解大脑及其疾病，并利用这些知识构建新的计算机技术。这个项目的三个主要目标是: 更好地理解大脑的各个部分是如何相互匹配和协同工作的; 理解如何客观地诊断和治疗脑部疾病; 以及利用对人类大脑的理解来开发神经形态计算机。模拟一个完整的人类大脑需要一台比现在强大一千倍的超级计算机，这鼓励了当前对神经形态计算机的关注。欧洲委员会已经拨款13亿美元用于这个项目。
Other research with implications for neuromorphic engineering involves the [[BRAIN Initiative]]<ref name="economist">[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</ref> and the [[TrueNorth]] chip from [[IBM]].<ref>{{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}}</ref> Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.<ref>{{Cite web|url=http://jessamynfairfield.com/wp-content/uploads/2017/03/PWMar17Fairfield.pdf|title=Smarter Machines|last=Fairfield|first=Jessamyn|date=March 1, 2017}}</ref>
Other research with implications for neuromorphic engineering involves the BRAIN InitiativeNeuromorphic computing: The machine of a new soul, The Economist, 2013-08-03 and the TrueNorth chip from IBM. Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.
其他与神经形态工程有关的研究还包括 BRAIN initiativity/euromorphic computing: The machine of a new soul，The Economist，2013-08-03 and The TrueNorth chip from IBM。神经形态学设备也已经被证明使用纳米晶体、纳米线和导电聚合物。
[[Intel]] unveiled its neuromorphic research chip, called “[[Intel Loihi|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.<ref>{{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 }}</ref><ref name="Morris2017">{{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}}</ref>
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.
2017年10月，英特尔公布了其神经形态研究芯片“ Loihi”。该芯片采用异步脉冲神经网络(SNN)实现自适应自修改事件驱动的细粒度并行计算，实现了高效的学习和推理。
[[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.<ref>{{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}}</ref> 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.<ref>{{cite web|last1=Bourzac|first1=Katherine|title=A Neuromorphic Chip That Makes Music|url=https://spectrum.ieee.org/a-neuromorphic-chip-that-makes-music|url-status=live|access-date=1 October 2019|website=IEEE Spectrum|date=May 23, 2017}}</ref>
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. 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.
比利时的一个纳米电子学研究中心 IMEC 展示了世界上第一个自学习神经形态芯片。这种基于 OxRAM 技术的大脑启发芯片具有自学习能力，并已被证明具有创作音乐的能力。IMEC 发布了由原型机谱写的30秒曲调。芯片按顺序加载同时签名和风格的歌曲。歌曲是古老的比利时和法国长笛小步舞曲，筹码从中学习游戏规则，然后应用它们。
[[Blue Brain Project|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 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.
由亨利 · 马克拉姆领导的蓝脑计划旨在建立小鼠大脑的生物细节数字重建和模拟。蓝色大脑项目已经在啮齿动物大脑的电子模型中创建，同时试图尽可能多地复制其生物学细节。基于超级计算机的模拟为理解大脑的结构和功能提供了新的视角。
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) that of their biological counterparts.<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>
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) that of their biological counterparts.
欧盟资助了海德堡大学的一系列项目，这些项目导致了 BrainScaleS (神经形态混合系统中受大脑启发的多尺度计算)的发展，这是一台位于德国海德堡大学的混合模拟神经形态超级计算机。它是作为人脑计划神经形态计算平台的一部分而开发的，是 SpiNNaker 超级计算机(基于数字技术)的补充。大脑尺度中使用的体系结构模拟了生物神经元及其在物理层面上的连接; 此外，由于这些组件是由硅制成的，这些模型神经元平均运行864倍(在机器模拟中，24小时的实时时间是100秒)。
===Neuromorphic sensors===
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]].
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.
= = = 神经形态传感器 = = = 神经形态系统的概念可以扩展到传感器(而不仅仅是计算)。用于检测光线的一个例子是视网膜变形传感器，或者在阵列中使用的事件摄像机。
== Ethical considerations ==
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.
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.
虽然神经形态工程的跨学科概念相对较新，但许多同样的伦理考虑适用于神经形态系统，就像适用于类人机器和一般人工智能一样。然而，神经形态系统是为了模仿人类大脑而设计的这一事实引起了围绕其使用的独特的伦理问题。
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 [[complex system|complexity in terms of size and functional technology]] and a much more regular structure in terms of [[brain connectivity|connectivity]]. Comparing [[neuromorphic chip]]s 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 neural cognitive systems are many orders of magnitude more [[Energy efficiency (physics)|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 [[bioengineering|bio-inspired features]].
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 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.
然而，实际上的争论是，神经形态硬件和人工“神经网络”是大脑如何运作或处理信息的极其简化的模型，在大小和功能技术方面的复杂性要低得多，在连接方面的结构则更加规则。将神经形态芯片与大脑进行比较是一种非常粗糙的比较，类似于仅仅因为一架飞机有翅膀和一条尾巴就将它与一只鸟进行比较。事实上，神经认知系统比当前最先进的人工智能具有更多的能量和计算效率，而神经形态工程是一种通过从大脑机制中激发灵感来缩小这种差距的尝试，就像许多工程设计具有生物启发的特征一样。数量级。
=== Democratic concerns ===
Significant ethical limitations may be placed on neuromorphic engineering due to public perception.<ref>{{Cite report|url=https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai_100_report_0831fnl.pdf|title=Artificial Intelligence and Life in 2030|author=2015 Study Panel|date=September 2016|work=One Hundred Year Study on Artificial Intelligence (AI100)|publisher=Stanford University}}</ref> 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.<ref name=":1">{{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}}</ref>
Significant ethical limitations may be placed on neuromorphic engineering due to public perception. 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.
由于公众的认知，神经形态工程学可能会受到严重的伦理限制。欧盟委员会进行的一项调查发现，60% 的欧盟公民希望禁止照顾儿童、老人或残疾人的机器人。此外，34% 的人支持禁止机器人用于教育，27% 的人支持医疗保健，20% 的人支持休闲。欧盟委员会将这些地区明显归类为“人类”报告指出，公众越来越关注能够模仿或复制人类功能的机器人。神经形态工程，顾名思义，是为了复制人脑的功能而设计的。
The democratic 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.<ref name=":1" />
The democratic 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.
围绕神经形态工程的民主关注可能在未来变得更加深刻。欧盟委员会(European Commission)发现，15至24岁的欧盟公民比55岁以上的欧盟公民更有可能认为机器人像人(而不是像仪器)。当看到一张被定义为“类人”的机器人图片时，年龄在15岁至24岁之间的欧盟公民中有75% 的人表示，这与他们对机器人的想法相符，而55岁以上的欧盟公民中只有57% 的人有同样的反应。因此，类似人类的神经形态系统，可以把它们归入许多欧盟公民希望在未来禁止使用的机器人类别。
=== Personhood ===
As neuromorphic systems have become increasingly advanced, some scholars{{who|date=August 2021}} have advocated for granting [[personhood]] rights to these systems. If the brain is what grants humans their personhood, to what extent does a neuromorphic system have to mimic the human brain to be granted personhood rights? Critics of technology development in the [[Human Brain Project]], which aims to advance brain-inspired computing, have argued that advancement in neuromorphic computing could lead to machine consciousness or personhood.<ref>{{Cite journal|last=Aicardi|first=Christine|date=September 2018|title=Accompanying technology development in the Human Brain Project: From foresight to ethics management|journal=Futures|volume=102|pages=114–124|doi=10.1016/j.futures.2018.01.005|doi-access=free}}</ref> If these systems are to be treated as people, critics argue, then many tasks humans perform using neuromorphic systems, including the act of termination of neuromorphic systems, may be morally impermissible as these acts would violate the autonomy of the neuromorphic systems.<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>
As neuromorphic systems have become increasingly advanced, some scholars have advocated for granting personhood rights to these systems. If the brain is what grants humans their personhood, to what extent does a neuromorphic system have to mimic the human brain to be granted personhood rights? Critics of technology development in the Human Brain Project, which aims to advance brain-inspired computing, have argued that advancement in neuromorphic computing could lead to machine consciousness or personhood. If these systems are to be treated as people, critics argue, then many tasks humans perform using neuromorphic systems, including the act of termination of neuromorphic systems, may be morally impermissible as these acts would violate the autonomy of the neuromorphic systems.
= = = 人格 = = 随着神经形态系统的日益发展，一些学者主张赋予这些系统人格权。如果是大脑赋予了人类人格，那么神经形态系统在多大程度上必须模仿人类大脑才能被赋予人格权利？“人脑计划”旨在推进以大脑为灵感的计算机技术发展，该计划的批评者认为，神经形态计算机技术的进步可能导致机器意识或人格的形成。批评家认为，如果这些系统被当作人来对待，那么人类使用神经形态系统执行的许多任务，包括终止神经形态系统的行为，可能在道德上是不允许的，因为这些行为将违反神经形态系统的自主性。
==Dual use (military applications) ==
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 fighter every shooter" within a network of neuromorphic-enabled units.
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 fighter every shooter" within a network of neuromorphic-enabled units.
军民两用联合人工智能中心是美国军队的一个分支，专门从事采购和实施用于战斗的人工智能软件和神经形态硬件。具体应用包括智能耳机/护目镜和机器人。JAIC 打算严重依赖神经形态技术来连接神经形态单位网络中的“每个战士每个射手”。
== Legal considerations ==
Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the [[Natural person|Natural Person]] model, since the robot would then hold [[human rights]], such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the [[Charter of Fundamental Rights of the European Union]] and the [[Convention for the Protection of Human Rights and Fundamental Freedoms]].”<ref>{{Cite web|url=http://www.robotics-openletter.eu/|title=Robotics Openletter {{!}} Open letter to the European Commission|language=fr-FR|access-date=2019-05-10}}</ref>
Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the Natural Person model, since the robot would then hold human rights, such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the Charter of Fundamental Rights of the European Union and the Convention for the Protection of Human Rights and Fundamental Freedoms.”
= = 法律方面的考虑 = = 怀疑论者认为，在法律上没有办法应用电子人格，这个人格概念将适用于神经形态技术。在一封由285名法律、机器人、医学和伦理学专家签名的信中，作者们反对欧盟委员会承认“智能机器人”为法人的提议，他们写道，“机器人的法律地位不能从自然人模型中推导出来，因为机器人将拥有人权，如尊严权、完整权、报酬权或公民权，从而直接面对人权。这将有悖于《欧洲联盟基本权利宪章和《保护人权和基本自由公约》。”
=== Ownership and property rights ===
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|Material Safety Data Sheets]] could not be [[Copyright law of Australia|copyrighted]] as it was generated by a [[software interface]] rather than a human author.<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> 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?<ref>{{cite journal |last1=Eshraghian|first1=Jason K. |title=Human Ownership of Artificial Creativity |journal=Nature Machine Intelligence |date=9 March 2020 |volume=2 |pages=157–160 |doi=10.1038/s42256-020-0161-x}}</ref>
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. 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?
= = = 所有权和财产权 = = = 围绕财产权和人工智能有着重大的法律争论。在 Acohs Pty Ltd 诉 Ucorp Pty Ltd 一案中，澳大利亚联邦法院的克里斯托弗 · 杰瑟普法官发现，材料安全数据表的源代码不能受版权保护，因为它是由软件界面而不是人工作者生成的。同样的问题可能也适用于神经形态系统: 如果一个神经形态系统成功地模仿了人类的大脑并产生了一部原创作品，那么谁，如果有人，应该声称拥有这部作品的所有权？
==Neuromemristive systems==
Neuromemristive systems are a subclass of neuromorphic computing systems that focus on the use of [[memristors]] to implement [[neuroplasticity]]. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction.<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> For example, a neuromemristive system may replace the details of a [[Cerebral cortex|cortical]] microcircuit's behavior with an abstract neural network model.<ref>C. Merkel and D. Kudithipudi, "Neuromemristive extreme learning machines for pattern classification," ISVLSI, 2014.</ref>
Neuromemristive systems are a subclass of neuromorphic computing systems that focus on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction. 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.
神经记忆电阻系统是神经形态计算系统的一个亚类，主要研究利用记忆电阻器实现神经可塑性。神经形态工程的重点是模拟生物行为，而神经记忆电阻系统的重点是提取。例如，一个神经记忆系统可以用一个抽象的神经网络模型取代皮层微电路的行为细节。默克尔和 d. Kudithipudi，“用于模式分类的神经记忆极端学习机器”，ISVLSI，2014。
There exist several neuron inspired threshold logic functions<ref name="Maan 1–13"/> implemented with memristors that have applications in high level [[pattern recognition]] applications. Some of the applications reported recently include [[speech recognition]],<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|url = https://semanticscholar.org/paper/48d3ab11ec6e213b62f11eedcfb7b7febb058674|hdl-access = free}}</ref> [[face recognition]]<ref>{{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}}</ref> and [[object recognition]].<ref>{{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}}</ref> They also find applications in replacing conventional digital logic gates.<ref>{{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}}</ref><ref>{{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}}</ref>
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, face recognition and object recognition. They also find applications in replacing conventional digital logic gates.
利用记忆电阻器实现的神经元启发阈值逻辑函数在高级模式识别中有着广泛的应用。最近报道的一些应用包括语音识别、人脸识别和物体识别。它们还可以用来取代传统的数字逻辑门。
For ideal passive memristive circuits there is an exact equation (Caravelli-Traversa-[[Di Ventra]] equation) for the internal memory of the circuit:<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 |pages= 022140 |journal=Physical Review E|bibcode=2017PhRvE..95b2140C |s2cid=6758362}}</ref>
For ideal passive memristive circuits there is an exact equation (Caravelli-Traversa-Di Ventra equation) for the internal memory of the circuit:
对于理想的无源记忆电路，电路的内部记忆有一个精确的方程(Caravelli-Traversa-Di Ventra 方程) :
:<math> \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S </math>
: \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
: \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
as a function of the properties of the physical memristive network and the external sources. In the equation above, <math>\alpha</math> is the "forgetting" time scale constant, <math> \xi=r-1</math> and <math>r=\frac{R_\text{off}}{R_\text{on}}</math> is the ratio of ''off'' and ''on'' values of the limit resistances of the memristors, <math> \vec S </math> is the vector of the sources of the circuit and <math>\Omega</math> is a projector on the fundamental loops of the circuit. The constant <math>\beta</math> 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 <math>W=\operatorname{diag}(\vec W)</math> and <math>\vec W</math> 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.
as a function of the properties of the physical memristive network and the external sources. In the equation above, \alpha is the "forgetting" time scale constant, \xi=r-1 and r=\frac{R_\text{off}}{R_\text{on}} is the ratio of off and on values of the limit resistances of the memristors, \vec S is the vector of the sources of the circuit and \Omega is a projector on the fundamental loops of the circuit. The constant \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 W=\operatorname{diag}(\vec W) and \vec W 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.
作为物理记忆网络和外部源的性质的函数。在上述方程中，α 是“遗忘”时间尺度常数，xi = r-1，r = frac { r _ text { off }{ on }{ r _ text { on }}是记忆电阻器极限电阻的开关和开关值之比，vec s 是电路源的矢量，Omega 是电路基本环路的投影仪。常数 β 具有电压的尺寸，与记忆电阻器的特性有关; 它的物理起源是导体中的电荷迁移率。对角矩阵和向量 w = 操作者名{ diag }(vec w)和 vec w 分别是记忆电阻器的内值，值在0到1之间。因此，这个等式需要在内存值上添加额外的约束，以保证可靠性。
==See also==
{{Columns-list|colwidth=18em|
* [[AI accelerator (computer hardware)|AI accelerator]]
* [[Artificial brain]]
* [[Biomorphic]]
* [[Cognitive computer]]
* [[Computation and Neural Systems]]
* [[Differentiable programming]]
* [[Event camera]]
* [[Neurorobotics]]
* [[Optical flow sensor]]
* [[Physical neural network]]
* [[SpiNNaker]]
* [[SyNAPSE]]
* [[Retinomorphic sensor]]
* [[Vision chip]]
* [[Vision processing unit]]
* [[Zeroth (software)]]
* [[Hardware for artificial intelligence]]
}}
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==References==
{{Reflist|40em}}
==External links==
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*[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]].
*[http://www.humanbrainproject.eu/ Human Brain Project official site]
* [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. May 1, 2019. SANDEEP RAVINDRAN
*Telluride Neuromorphic Engineering Workshop
*CapoCaccia Cognitive Neuromorphic Engineering Workshop
*Institute of Neuromorphic Engineering
*INE news site.
*Frontiers in Neuromorphic Engineering Journal
*Computation and Neural Systems department at the California Institute of Technology.
*Human Brain Project official site
* Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN
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* 打造硅脑: 基于生物神经元的计算机芯片可能有助于模拟更大、更复杂的大脑模型。2019年5月1日。SANDEEP RAVINDRAN
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