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神经形态计算 (查看源代码)

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'''神经形态工程Neuromorphic engineering'''（也称为'''神经形态计算Neuromorphic computing'''）<ref name=":3" /><ref name=":4" /><ref name="humanbrainproject" />是指使用包含电子'''模拟电路Analog circuit'''的'''超大规模集成电路Very-large-scale integration'''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'''神经系统Neural system'''模型功能(如'''感知Perception'''、'''运动控制Motor control'''，'''多感官整合Multisensory integration'''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'''记忆电阻器Memristor'''、<ref name="Maan 1–13" />自旋电子存储器、阈值开关和'''晶体管Transistor'''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" />

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'''神经形态工程Neuromorphic engineering'''（也称为'''神经形态计算Neuromorphic computing'''或'''类脑计算Brain-inspired computing'''）<ref name=":3" /><ref name=":4" /><ref name="humanbrainproject" />是指使用包含电子'''模拟电路Analog circuit'''的'''超大规模集成电路Very-large-scale integration'''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'''神经系统Neural system'''模型功能(如'''感知Perception'''、'''运动控制Motor control'''，'''多感官整合Multisensory integration'''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'''记忆电阻器Memristor'''（简称忆阻器）、<ref name="Maan 1–13" />自旋电子存储器、阈值开关和'''晶体管Transistor'''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.

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神经形态工程是以'''生物学Biology'''、'''物理学Physics'''、'''数学Mathematics'''、'''计算机科学Computer science'''和'''电子工程Electronic engineering<ref name=":2" />'''等学科为基础，设计人工神经系统（如'''视觉系统Vision systems'''、头眼系统、听觉处理器以及物理结构和设计原则都受启发于生物神经系统的自主机器人）的一门交叉学科。<ref name=":9" />20世纪80年代后期，卡弗·米德极大地推动了神经形态工程领域的发展。<ref name=":10" />

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==Neurological inspiration==

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==Neurological inspiration ==

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==神经生物学启发==

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==神经生物学启发 ==

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]].

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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>

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2012年6月，普渡大学的'''自旋电子学Spintronic'''研究人员发表了一篇关于利用'''侧向自旋阀Lateral spin valves'''和'''~~记忆电阻器Memristors~~'''设计神经形态芯片的论文。他们认为，这种芯片结构的工作原理与神经元相似，因此可以用于大脑运行机制的复刻方法的测试。此外，这些芯片在能耗方面明显优于传统芯片。<ref name="Spin Devices Prop" />

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2012年6月，普渡大学的'''自旋电子学Spintronic'''研究人员发表了一篇关于利用'''侧向自旋阀Lateral spin valves'''和'''忆阻器Memristors'''设计神经形态芯片的论文。他们认为，这种芯片结构的工作原理与神经元相似，因此可以用于大脑运行机制的复刻方法的测试。此外，这些芯片在能耗方面明显优于传统芯片。<ref name="Spin Devices Prop" />

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>

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'''惠普实验室HP labs'''~~在莫特记忆电阻器上的研究表明，尽管它们可以是非~~'''易失性Volatile'''的，但是在'''相变Phase transition'''温度以下时表现出的易失性行为可以被用来制造'''类神经元电阻器Neuristor'''（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'''图灵机Turing machine'''的所需元素。<ref name=":14" />

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'''惠普实验室HP labs'''在莫特忆阻器上的研究表明，尽管它们可以是非'''易失性Volatile'''的，但是在'''相变Phase transition'''温度以下时表现出的易失性行为可以被用来制造'''类神经元电阻器Neuristor'''（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'''图灵机Turing machine'''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>

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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'''神经形态Neuromorphic'''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'''SpiNNaker'''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" />

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===Neuromorphic sensors===

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===Neuromorphic sensors ===

===神经形态传感器===

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]].

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==Ethical considerations ==

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==Ethical considerations==

==伦理问题==

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.

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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" />

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=== Personhood===

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===Personhood===

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=== 人格权问题===

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===人格权问题===

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 name=":28">{{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 name=":29">{{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>

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==Neuromemristive systems==

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==~~神经记忆电阻系统~~==

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==神经忆阻系统==

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 name=":33">{{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 name=":34">C. Merkel and D. Kudithipudi, "Neuromemristive extreme learning machines for pattern classification," ISVLSI, 2014.</ref>

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~~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Memristors~~'''实现'''神经可塑性Neuroplasticity'''~~。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。~~<ref name=":33" />~~举个例子，一个神经记忆系统可能用抽象的神经网络模型替代~~'''皮层Cortical'''微电路的行为细节。<ref name=":34" />

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神经忆阻系统是神经形态计算系统的一个亚类，主要研究利用'''忆阻器Memristors'''实现'''神经可塑性Neuroplasticity'''。神经形态工程的重点是模拟生物行为，而神经忆阻系统的重点是提取。<ref name=":33" />举个例子，一个神经忆阻系统可能用抽象的神经网络模型替代'''皮层Cortical'''微电路的行为细节。<ref name=":34" />

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 name=":35">{{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 name=":36">{{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 name=":37">{{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 name=":38">{{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 name=":39">{{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>

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~~受神经元启发、使用记忆电阻器实现的阈值逻辑函数~~<ref name="Maan 1–13" />在高级'''模式识别Pattern recognition'''中有着广泛的应用，最近报道中其应用包括'''语音识别Speech recognition<ref name=":35" />'''、'''人脸识别Face recognition<ref name=":36" />'''和'''物体识别Object recognition<ref name=":37" />'''。阈值逻辑函数还可以用来取代传统的数字逻辑门。<ref name=":38" /><ref name=":39" />

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受神经元启发、使用忆阻器实现的阈值逻辑函数<ref name="Maan 1–13" />在高级'''模式识别Pattern recognition'''中有着广泛的应用，最近报道中其应用包括'''语音识别Speech recognition<ref name=":35" />'''、'''人脸识别Face recognition<ref name=":36" />'''和'''物体识别Object recognition<ref name=":37" />'''。阈值逻辑函数还可以用来取代传统的数字逻辑门。<ref name=":38" /><ref name=":39" />

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>

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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.

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Caravelli-Traversa-Di Ventra方程是描述物理记忆网络和外部源性质的函数。在上述方程中，<math>\alpha</math>是“遗忘”时间尺度常数，<math>\xi=r-1</math>，<math>r =\frac{R\text_{off}}{R_\text{on}}</math>是记忆电阻器off状态和on状态极限电阻值之比，<math>\vec S</math>是电路源的矢量，<math>\Omega</math>是电路基本环路的投影。常数<math>\beta</math>具有电压的量纲，与记忆电阻器的特性有关；其物理原型是导体中的电荷迁移率。对角矩阵和向量 <math>W=\operatorname{diag}(\vec W)</math>和<math>\vec W</math> '''~~分别是记忆电阻器的内阻~~'''，值在0到1之间。因此，这个等式需要在'''内存值'''上添加额外约束以保证可靠性。

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Caravelli-Traversa-Di Ventra方程是描述物理记忆网络和外部源性质的函数。在上述方程中，<math>\alpha</math>是“遗忘”时间尺度常数，<math>\xi=r-1</math>，<math>r =\frac{R\text_{off}}{R_\text{on}}</math>是记忆电阻器off状态和on状态极限电阻值之比，<math>\vec S</math>是电路源的矢量，<math>\Omega</math>是电路基本环路的投影。常数<math>\beta</math>具有电压的量纲，与记忆电阻器的特性有关；其物理原型是导体中的电荷迁移率。对角矩阵和向量 <math>W=\operatorname{diag}(\vec W)</math>和<math>\vec W</math> '''分别是忆阻器的内阻'''，值在0到1之间。因此，这个等式需要在'''内存值'''上添加额外约束以保证可靠性。

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==External links==

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== External links ==

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*Institute of Neuromorphic Engineering

*INE news site.

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*Frontiers in Neuromorphic Engineering Journal

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* Frontiers in Neuromorphic Engineering Journal

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

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*Human Brain Project official site

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* 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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