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第81行： − 欧盟资助了海德堡大学一系列促进BrainScaleS(神经形态混合系统中受大脑启发的多尺度计算)发展的项目，这是一台位于德国海德堡大学的混合模拟'''<font color="#ff8000">神经形态Neuromorphic</font>'''超级计算机。它是作为人类大脑计划中神经形态计算平台的一部分而开发的，是'''<font color="#ff8000">SpiNNaker</font>'''超级计算机(基于数字技术)的补充。BrainScaleS中使用的体系架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速度是生物神经元的864倍，这意味着在机器模拟中，24小时的实时时间仅为100秒。<ref name=":26" />+ 第87行：
第87行： − 神经形态系统的概念可以扩展到传感器(而不仅仅是计算单元)。一个用于检测光线的例子是'''<font color="#ff8000">类视网膜传感器Retinomorphic sensor</font>'''，或者'''<font color="#ff8000">事件摄像机Event camera</font>阵列。'''+ 第104行：
第104行： − + − 围绕神经形态工程的公众担忧可能在未来变得更加严重。欧盟委员会发现，相比于55岁以上的欧盟公民，15至24岁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“类人”机器人的图片时，年龄在15岁至24岁之间的欧盟公民中有75% 的人表示这符合他们对机器人的想法，而55岁以上的欧盟公民中只有57% 的人有同样的反应。因此，神经形态系统可能因为其类似人类的特性而被归入许多欧盟公民希望在未来禁止使用的机器人类别。<ref name=":1" />+ 第121行：
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第149行： − 对于理想的无源记忆电路，电路的内部记忆可以用精确的方程(Caravelli-Traversa-Di Ventra方程) 来描述:<ref name=":40" />+
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'''<font color="#ff8000">神经形态工程Neuromorphic engineering</font>'''(也称为'''<font color="#ff8000">神经形态计算Neuromorphic computing</font>''')<ref name=":3" /><ref name=":4" /><ref name="humanbrainproject" />是指使用包含电子'''<font color="#ff8000">模拟电路Analog circuit</font>'''的'''<font color="#ff8000">超大规模集成电路Very-large-scale integration</font>'''系统来模拟神经系统中生理结构的研究方法。神经形态计算机或神经形态芯片包括任何使用由硅制成的人造神经元进行计算的设备。<ref name=":5" /><ref name=":2" />近年来，神经形态学（neuromorphic）这个术语被用来描述能够实现'''<font color="#ff8000">神经系统Neural system</font>'''模型功能(如'''<font color="#ff8000">感知Perception</font>'''、'''<font color="#ff8000">运动控制Motor control</font>'''，'''<font color="#ff8000">多感官整合Multisensory integration</font>'''等)的模拟、数字、模拟/数字混合模式超大规模集成电路和软件系统。神经形态计算的硬件实现可以通过基于氧化物的'''<font color="#ff8000">记忆电阻器Memristor</font>'''、<ref name="Maan 1–13" />自旋电子存储器、阈值开关和'''<font color="#ff8000">晶体管Transistor</font>'''来实现。<ref name=":6" /><ref name=":2" />对基于软件的脉冲神经网络系统的训练可以通过误差反向传播机制来实现，例如，使用snnTorch等基于Python的框架，<ref name=":7" />或使用BindsNet等典型的受生物启发的学习模式。<ref name=":8" />
'''<font color="#ff8000">神经形态工程Neuromorphic engineering</font>'''（也称为'''<font color="#ff8000">神经形态计算Neuromorphic computing</font>'''）<ref name=":3" /><ref name=":4" /><ref name="humanbrainproject" />是指使用包含电子'''<font color="#ff8000">模拟电路Analog circuit</font>'''的'''<font color="#ff8000">超大规模集成电路Very-large-scale integration</font>'''系统来模拟神经系统中生理结构的研究方法。神经形态计算机或神经形态芯片包括任何使用由硅制成的人造神经元进行计算的设备。<ref name=":5" /><ref name=":2" />近年来，神经形态学（neuromorphic）这个术语被用来描述能够实现'''<font color="#ff8000">神经系统Neural system</font>'''模型功能(如'''<font color="#ff8000">感知Perception</font>'''、'''<font color="#ff8000">运动控制Motor control</font>'''，'''<font color="#ff8000">多感官整合Multisensory integration</font>'''等)的模拟、数字、模拟/数字混合模式超大规模集成电路和软件系统。神经形态计算的硬件实现可以通过基于氧化物的'''<font color="#ff8000">记忆电阻器Memristor</font>'''、<ref name="Maan 1–13" />自旋电子存储器、阈值开关和'''<font color="#ff8000">晶体管Transistor</font>'''来实现。<ref name=":6" /><ref name=":2" />对基于软件的脉冲神经网络系统的训练可以通过误差反向传播机制来实现，例如，使用snnTorch等基于Python的框架，<ref name=":7" />或使用BindsNet等典型的受生物启发的学习模式。<ref name=":8" />
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.
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 name=":14">{{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 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 name=":14">{{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>
'''<font color="#ff8000">惠普实验室HP labs</font>'''在莫特记忆电阻器上的研究表明，尽管它们可以是非'''<font color="#ff8000">易失性Volatile</font>'''的，但是在'''<font color="#ff8000">相变Phase transition</font>'''温度以下时表现出的易失性行为可以被用来制造'''<font color="#ff8000">类神经元电阻器Neuristor</font>'''(一种生物学启发的模仿神经元行为的硬件)<ref name=":0" />。2013年9月，他们通过模型和仿真展示了这些类神经元电阻器的脉冲行为如何产生'''<font color="#ff8000">图灵机Turing machine</font>'''的所需元素。<ref name=":14" />
'''<font color="#ff8000">惠普实验室HP labs</font>'''在莫特记忆电阻器上的研究表明，尽管它们可以是非'''<font color="#ff8000">易失性Volatile</font>'''的，但是在'''<font color="#ff8000">相变Phase transition</font>'''温度以下时表现出的易失性行为可以被用来制造'''<font color="#ff8000">类神经元电阻器Neuristor</font>'''（一种生物学启发的模仿神经元行为的硬件）<ref name=":0" />。2013年9月，他们通过模型和仿真展示了这些类神经元电阻器的脉冲行为如何产生'''<font color="#ff8000">图灵机Turing machine</font>'''的所需元素。<ref name=":14" />
[[Neurogrid]], built by ''Brains in Silicon'' at [[Stanford University]],<ref name=":15">{{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 name=":16">{{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 name=":17">{{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]],<ref name=":15">{{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 name=":16">{{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 name=":17">{{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>
'''<font color="#ff8000">神经栅格Neurogrid<ref name=":15" /></font>'''是由斯坦福大学Brains in Silicon公司研发的、使用神经形态工程原理设计的硬件。该电路板由16个定制设计的芯片组成(NeuroCores)。在设计中，每个NeuroCore芯片的模拟电路对65536个神经元的神经元素进行模拟，以最大限度地提高能量效率。模拟出的神经元通过设计的数字电路连接，以最大化脉冲吞吐量。<ref name=":16" /><ref name=":17" />
'''<font color="#ff8000">神经栅格Neurogrid<ref name=":15" /></font>'''是由斯坦福大学Brains in Silicon公司研发的、使用神经形态工程原理设计的硬件。该电路板由16个定制设计的芯片组成（NeuroCores）。在设计中，每个NeuroCore芯片的模拟电路对65536个神经元的神经元素进行模拟，以最大限度地提高能量效率。模拟出的神经元通过设计的数字电路连接，以最大化脉冲吞吐量。<ref name=":16" /><ref name=":17" />
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 name=":18">{{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 name=":19">{{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 name=":20">{{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.<ref name=":18">{{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 name=":19">{{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 name=":20">{{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>
'''<font color="#ff8000">人类大脑计划Human Brain Project</font>'''对神经形态工程具有较大影响，其主要任务是尝试用生物数据在超级计算机中模拟完整的人脑。人类大脑计划由神经科学、医学和计算机科学背景的研究人员组成。<ref name=":18" />该项目的联合主管亨利•马克拉姆(Henry Markram)表示，人类大脑计划的目的是建立一个探索和了解脑科学和脑疾病知识的基础，并利用这些知识来构建更先进的计算机技术。这个项目的三个主要目标分别是: 更好地理解大脑的各个部分是如何相互配合协同工作的; 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，这不断激发着对神经形态计算机领域的研究兴趣。<ref name=":19" />欧盟委员会已经向人类大脑计划拨款13亿美元。<ref name=":20" />
'''<font color="#ff8000">人类大脑计划Human Brain Project</font>'''对神经形态工程具有较大影响，其主要任务是尝试用生物数据在超级计算机中模拟完整的人脑。人类大脑计划由神经科学、医学和计算机科学背景的研究人员组成。<ref name=":18" />该项目的联合主管亨利•马克拉姆（Henry Markram）表示，人类大脑计划的目的是建立一个探索和了解脑科学和脑疾病知识的基础，并利用这些知识来构建更先进的计算机技术。这个项目的三个主要目标分别是: 更好地理解大脑的各个部分是如何相互配合协同工作的; 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，这不断激发着对神经形态计算机领域的研究兴趣。<ref name=":19" />欧盟委员会已经向人类大脑计划拨款13亿美元。<ref name=":20" />
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 name=":21">{{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 name=":22">{{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 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 name=":21">{{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 name=":22">{{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>
[[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 name=":26">{{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>
[[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 name=":26">{{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>
欧盟资助了海德堡大学一系列促进BrainScaleS（神经形态混合系统中受大脑启发的多尺度计算）发展的项目，这是一台位于德国海德堡大学的混合模拟'''<font color="#ff8000">神经形态Neuromorphic</font>'''超级计算机。它是作为人类大脑计划中神经形态计算平台的一部分而开发的，是'''<font color="#ff8000">SpiNNaker</font>'''超级计算机（基于数字技术）的补充。BrainScaleS中使用的体系架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速度是生物神经元的864倍，这意味着在机器模拟中，24小时的实时时间仅为100秒。<ref name=":26" />
===Neuromorphic sensors===
===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]].
神经形态系统的概念可以扩展到传感器（而不仅仅是计算单元）。一个用于检测光线的例子是'''<font color="#ff8000">类视网膜传感器Retinomorphic sensor</font>'''，或者'''<font color="#ff8000">事件摄像机Event camera</font>阵列。'''
Significant ethical limitations may be placed on neuromorphic engineering due to public perception.<ref name=":27">{{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.<ref name=":27">{{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>
由于公众认知的相关忧虑，神经形态工程学可能会受到严重的伦理限制。<ref name=":27" />欧盟委员会进行的一项调查发现，60% 的欧盟公民希望禁止机器人参与照顾儿童、老人或残疾人的工作。此外，34% 的人支持禁止机器人用于教育，27% 的人支持禁止机器人用于医疗保健，20% 的人支持禁止机器人用于娱乐。欧盟委员会将以上领域划入“人类”范畴。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。而神经形态工程，顾名思义，是为了模仿人脑的功能而设计的。<ref name=":1" />
由于公众认知的相关忧虑，神经形态工程学可能会受到严重的伦理限制。<ref name=":27" />欧盟委员会进行的一项调查发现，60% 的欧盟公民希望禁止机器人参与照顾儿童、老人或残疾人的工作。此外，34% 的人支持禁止机器人用于教育，27% 的人支持禁止机器人用于医疗保健，20% 的人支持禁止机器人用于娱乐。欧盟委员会将以上领域划入“人类”范畴。报告指出，公众越来越关注能够模仿或复制人类行为的机器人。而神经形态工程，顾名思义，是为了模仿人脑的功能而设计的。<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.<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.<ref name=":1" />
围绕神经形态工程的公众担忧可能在未来变得更加严重。欧盟委员会发现，相比于55岁以上的欧盟公民，15至24岁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“类人”机器人的图片时，年龄在15岁至24岁之间的欧盟公民中有75% 的人表示这符合他们对机器人的想法，而55岁以上的欧盟公民中只有57% 的人有同样的反应。因此，神经形态系统可能因为其类似人类的特性而被归入许多欧盟公民希望在未来禁止使用的机器人类别。<ref name=":1" />
=== Personhood===
=== Personhood===
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.
'''<font color="#ff8000">联合人工智能中心The Joint Artificial Intelligence Center</font>'''(JAIC)，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打算高度依赖神经形态技术，'''<font color="#32CD32">使用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”</font>'''。
'''<font color="#ff8000">联合人工智能中心The Joint Artificial Intelligence Center</font>'''（JAIC），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打算高度依赖神经形态技术，'''<font color="#32CD32">使用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”</font>'''。
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 name=":31">{{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 name=":32">{{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|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 name=":31">{{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 name=":32">{{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>
法律界围绕财产权和人工智能有着重大争论。在Acohs Pty有限公司诉Ucorp Pty有限公司一案中，澳大利亚联邦法院的克里斯托弗·杰瑟普法官发现，版权保护不适用于材料安全数据表的'''<font color="#ff8000">源代码Source code</font>'''，因为它是由'''<font color="#ff8000">软件界面Software interface</font>'''生成而非人类工作者生成的。<ref name=":31" />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，那么该如何确认这部作品的所有权归属？<ref name=":32" />
法律界围绕财产权和人工智能有着重大争论。在Acohs Pty有限公司诉Ucorp Pty有限公司一案中，澳大利亚联邦法院的克里斯托弗·杰瑟普法官发现，版权保护不适用于材料安全数据表的'''<font color="#ff8000">源代码Source code</font>'''，因为它是由'''<font color="#ff8000">软件界面Software interface</font>'''生成而非人类工作者生成的。<ref name=":31" />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，那么该如何确认这部作品的所有权归属？<ref name=":32" />
For ideal passive memristive circuits there is an exact equation (Caravelli-Traversa-[[Di Ventra]] equation) for the internal memory of the circuit:<ref name=":40">{{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:<ref name=":40">{{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>
对于理想的无源记忆电路，电路的内部记忆可以用精确的方程（Caravelli-Traversa-Di Ventra方程) 来描述:<ref name=":40" />
:<math> \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S </math>
:<math> \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S </math>