更改

 

{{Use American English|date = January 2019}}

{{Use American English|date = January 2019}}

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.

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。

'''<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>''')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。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>'''、自旋电子存储器、阈值开关和'''<font color="#ff8000">晶体管Transistor</font>'''来实现。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，或使用BindsNet等典型的受生物启发的学习模式。

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.

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。

神经形态工程领域的一个关键问题，就是理解'''<font color="#32CD32">单个神经元形态、神经回路、应用和整体结构</font>'''如何产生理想的计算，如何影响信息的表示和对破坏的鲁棒性，如何整合学习和发展，如何适应局部变化(可塑性) 并促进逐渐发展的变化。

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]]<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.

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。

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==

== 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 [[neuron]]s in [[cognition]].

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。

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==

== 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.<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.

= = = 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。

= = = 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 ==

==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.

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。

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 ===

=== 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.<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>

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。

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 ===

===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{{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>

= = = 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。

= = = 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) ==

==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”。

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 ==

==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|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>

= = 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》。”

= = 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 ===

===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|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>

= = = 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？

= = = 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==

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.<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>

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。

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<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.

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.

:<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>

: \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

: \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, <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.

<nowiki>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.</nowiki>

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。

<nowiki>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。</nowiki>

==See also==

== See also==

{{Columns-list|colwidth=18em|

{{Columns-list|colwidth=18em|

* [[AI accelerator (computer hardware)|AI accelerator]]

* [[AI accelerator (computer hardware)|AI accelerator]]

*[http://www.cns.caltech.edu/ Computation and Neural Systems] department at the [[California Institute of Technology]].

*[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]

*[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

*[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

 

*Computation and Neural Systems department at the California Institute of Technology.

*Computation and Neural Systems department at the California Institute of Technology.

*Human Brain Project official site

*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

*Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN

= = = 外部链接 = =  

=<nowiki>= = 外部链接 =</nowiki>=  

* 碲化物神经形态工程工作室  

*碲化物神经形态工程工作室

* CapoCaccia 认知神经形态工程工作室  

*CapoCaccia 认知神经形态工程工作室

* 神经形态工程研究所  

*神经形态工程研究所

* INE 新闻站点。

*INE 新闻站点。

* 《神经形态工程学前沿》  

*《神经形态工程学前沿》

* 加州理工学院计算与神经系统系。

*加州理工学院计算与神经系统系。

* 人脑项目官方网站  

* 人脑项目官方网站

* 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

*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

{{Differentiable computing}}

{{Differentiable computing}}

[[Category:Electrical engineering]]

[[Category:Electrical engineering]]

[[Category:Neuroscience]]

[[Category:Neuroscience]]

[[Category:AI accelerators|*]]

[[分类:AI accelerators|*]]

[[Category:Artificial intelligence]]

[[Category:Artificial intelligence]]

[[Category:Robotics]]

[[Category:Robotics]]

[[Category:待整理页面]]

[[Category:待整理页面]]