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第1行： − {{short description|Network or circuit of neurons}} − {{About||neural networks in computers|artificial neural network|projections from one region of the nervous system to another|neural pathway}}关于计算机中使用的神经网络，请见<font color="#ff8000">人工神经网络artificial neural network</font>。关于神经系统中一处神经元与另一处神经元之间的连接通路，请见<font color="#ff8000">神经通路neural pathway</font>。[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] |链接=Special:FilePath/Blausen_0657_MultipolarNeuron.png]] − A '''neural circuit''' is a population of [[neuron]]s interconnected by [[synapse]]s to carry out a specific function when activated.<ref name="Neuro">{{cite book |last1=Purves |first1=Dale |title=Neuroscience |date=2011 |publisher=Sinauer |location=Sunderland, Mass. |isbn=9780878936953 |page=507 |edition= 5th}}</ref> Neural circuits interconnect to one another to form [[large scale brain networks]].<ref name="CEI">{{cite web |title=Neural Circuits {{!}} Centre of Excellence for Integrative Brain Function |url=https://www.brainfunction.edu.au/research/research-themes/neural-circuits/ |website=Centre of Excellence for Integrative Brain Function |access-date=4 June 2018 |language=en-AU |date=13 June 2016}}</ref> Biological [[neural network]]s have inspired the design of [[artificial neural network]]s, but artificial neural networks are usually not strict copies of their biological counterparts.+ − <font color="#ff8000">神经回路neural circuit</font>由一群<font color="#ff8000">神经元neuron</font>构成，通过<font color="#ff8000">突触synapse</font>相互连接，被激活时，就可以执行某种特定功能。<ref name="Neuro" /> 神经回路彼此再连接，形成<font color="#ff8000">大尺度脑网络large scale brain networks</font>。<ref name="CEI" /> <font color="#ff8000">生物神经网络Biological neural network</font>启发了<font color="#ff8000">人工神经网络artificial neural network</font>的设计，但人工神经网络通常不是机械地复制生物神经网络。 − − 文件：Blausen 0657 Multipolar Neuron 第14行：
第9行： − + − + − Early treatments of neural [[Biological network|networks]] can be found in [[Herbert Spencer]]'s ''Principles of Psychology'', 3rd edition (1872), [[Theodor Meynert]]'s ''[[Psychiatry]]'' (1884), [[William James]]' ''Principles of [[Psychology]]'' (1890), and [[Sigmund Freud]]'s Project for a Scientific Psychology (composed 1895).<ref name=":0">{{cite web |url=http://psych.stanford.edu/~jlm/papers/ThomasMcCIPCambEncy.pdf |title=Connectionist models of cognition |author1=Michael S. C. Thomas |author2=James L. McClelland |publisher=[[Stanford University]] |access-date=2015-08-31 |archive-url=https://web.archive.org/web/20150906120214/http://psych.stanford.edu/~jlm/papers/ThomasMcCIPCambEncy.pdf |archive-date=2015-09-06 |url-status=dead }}</ref> The first rule of neuronal learning was described by [[Donald Olding Hebb|Hebb]] in 1949, in the [[Hebbian theory]]. Thus, Hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and therefore either facilitate or inhibit [[neurotransmission|signal transmission]]. In 1959, the [[neuroscientist]]s, [[Warren Sturgis McCulloch]] and [[Walter Pitts]] published the first works on the processing of neural networks.<ref name=":1">{{citation | title = What the frog's eye tells the frog's brain. |author1=J. Y. Lettvin |author2=H. R. Maturana |author3=W. S. McCulloch |author4=W. H. Pitts | year = 1959 | work = Proc. Inst. Radio Engr. | issue = 47 | pages = 1940–1951 }}</ref> They showed theoretically that networks of artificial neurons could [[implementation|implement]] [[logic]]al, [[arithmetic]], and [[symbol]]ic functions. Simplified [[Biological neuron model|models of biological neurons]] were set up, now usually called [[perceptrons]] or [[artificial neurons]]. These simple models accounted for [[Summation (Neurophysiology)|neural summation]] (i.e., potentials at the post-synaptic membrane will summate in the [[cell body]]). Later models also provided for excitatory and inhibitory synaptic transmission. − = = 早期研究 = = 对神经网络的早期研究见于<font color="#ff8000">赫伯特 · 斯宾塞Herbert Spencer</font>的《心理学原理》第三版(1872)、<font color="#ff8000">西奥多 · 梅纳特Theodor Meynert</font>的《精神病学》(1884)、<font color="#ff8000">威廉 · 詹姆斯William James</font>的《心理学原理》(1890)和<font color="#ff8000">西格蒙德 · 弗洛伊德Sigmund Freud</font>的《科学心理学计划》(1895)。<ref name=":0" /> 1949年，<font color="#ff8000">赫布Hebb</font>在其理论（即<font color="#ff8000">赫布理论Hebbian theory</font>）中提出了神经元学习的第一定律。赫布理论认为，配对中突触前神经元和突触后神经元的活动可以充分改变突触连接的动态特性，即要么促进，要么抑制<font color="#ff8000">信号传递signal transmission</font>。1959年，<font color="#ff8000">神经科学家neuroscientist</font>, <font color="#ff8000">沃伦·麦卡洛克Warren Sturgis</font>和 <font color="#ff8000">沃尔特·皮茨Walter Pitts</font> 发表了关于神经网络处理的第一部著作。<ref name=":1" /> 他们从理论上证明了人工神经元网络可以实现<font color="#ff8000">逻辑logical</font>、<font color="#ff8000">算术arithmetic</font>和<font color="#ff8000">符号功能symbolic</font>。<font color="#ff8000">生物神经元的简化模型models of biological neurons</font>由此建立起来，现在通常被称为<font color="#ff8000">感知器perceptrons</font>或<font color="#ff8000">人工神经元artificial neurons</font>。这些简单的模型解释了<font color="#ff8000">神经加成作用neural summation</font>(即突触后膜上的电位将在<font color="#ff8000">细胞体cell body</font>中加成)。后来的模型也提供了兴奋性和抑制性突触传递。+ − 文件：Cajal actxinter − From "Texture of the [[Nervous System]] of Man and the [[Vertebrates]]" by [[Santiago Ramón y Cajal]]. The figure illustrates the diversity of neuronal morphologies in the [[auditory cortex]].+ − 摘自Santiago Ramón y Cajal的《人类和脊椎动物的神经系统结构》。这幅图说明了听觉皮层神经元形态的多样性。+ − − ==Connections between neurons== − {{See also|Synapse}}<nowiki>= = 神经元之间的连接 = =</nowiki> − − − + − Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013) − The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called [[neurexin]]s are central to this process.<ref name="Sudhof">{{cite journal |last1=Südhof |first1=TC |title=Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits. |journal=Cell |date=2 November 2017 |volume=171 |issue=4 |pages=745–769 |doi=10.1016/j.cell.2017.10.024 |pmid=29100073|pmc=5694349 }}</ref> − 突触的建立使得神经元能够连接成千上万个重叠的、相互连接的神经回路。被称为<font color="#ff8000">神经蛋白neurexin</font>的突触前蛋白在这一过程中起着核心作用。<ref name="Sudhof" />+ − One principle by which neurons work is [[Summation (neurophysiology)|neural summation]] – [[postsynaptic potential|potentials]] at the [[Chemical synapse|postsynaptic membrane]] will sum up in the cell body. If the [[depolarization]] of the neuron at the [[axon hillock]] goes above threshold an action potential will occur that travels down the [[axon]] to the terminal endings to transmit a signal to other neurons. Excitatory and inhibitory synaptic transmission is realized mostly by [[excitatory postsynaptic potentials]] (EPSPs), and [[inhibitory postsynaptic potentials]] (IPSPs). − 神经元工作的其中一个原理是<font color="#ff8000">神经加成neural summation</font>——<font color="#ff8000">突触后膜postsynaptic membrane</font>上的<font color="#ff8000">电位potential</font>将在细胞体中进行加成。如果<font color="#ff8000">神经元在轴突丘axon hillock</font>处的<font color="#ff8000">去极化depolarization</font>超过阈值，就会发生动作电位，动作电位沿着轴突向下传递到末端，将信号传递给其他神经元。兴奋性和抑制性突触传递主要通过<font color="#ff8000">兴奋性突触后电位exciatory postsynaptic potentials(EPSPs)</font>和<font color="#ff8000">抑制性突触后电位inhibitory postsynaptic potentials(IPSPs)</font>实现。+ − On the [[electrophysiology|electrophysiological]] level, there are various phenomena which alter the response characteristics of individual synapses (called [[synaptic plasticity]]) and individual neurons ([[intrinsic plasticity]]). These are often divided into short-term plasticity and long-term plasticity. Long-term synaptic plasticity is often contended to be the most likely [[memory]] substrate. Usually, the term "[[neuroplasticity]]" refers to changes in the brain that are caused by activity or experience. − 在<font color="#ff8000">电生理electrophysiological</font>层面上，存在着改变个体突触(<font color="#ff8000">突触可塑性synaptic plasticity</font>)和个体神经元(<font color="#ff8000">内禀可塑性intrinsic plasticity</font>)的反应特征的各种现象。这些可塑性通常分为短期可塑性和长期可塑性。长期突触可塑性通常被认为是最有可能的<font color="#ff8000">记忆memory</font>底物。通常来说，“<font color="#ff8000">神经可塑性neuroplasticity</font>”指的是由活动或经历引起的大脑变化。+ 第136行：
第120行： − ==See also== − *[[Feedback]] − *[[List of regions in the human brain]] − *[[Network science]] − *[[Neural coding]] − *[[Neural engineering]] − *[[Neural oscillation]] − *[[Pulse-coupled networks]] − *[[Systems neuroscience]] − 请见 − * 反馈 − * 人脑区域分布列表 − * 神经编码 − * 神经工程 − * 神经震荡 − * 脉冲耦合神经网络 − * 系统神经科学 − − ==References== − {{Reflist}} − − ==Further reading== − *[http://www.scholarpedia.org/article/Intrinsic_plasticity Intrinsic plasticity] Robert H. Cudmore, Niraj S. Desai [[Scholarpedia]] 3(2):1363. [[doi:10.4249/scholarpedia.1363]] − + − + + + + + + + − ==External links== − *[https://web.archive.org/web/20040807104201/http://www.his.sunderland.ac.uk/ps/worksh2/denham.pdf Comparison of Neural Networks in the Brain and Artificial Neural Networks] − *[https://web.archive.org/web/20051024152350/http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-95-AResearch-Topics-in-NeuroscienceJanuary--IAP-2003/LectureNotes/ Lecture notes at MIT OpenCourseWare] − *[http://www.willamette.edu/~gorr/classes/cs449/brain.html Computation in the Brain] − *[http://www.ymer.org/amir/software/biological-neural-networks-toolbox/ Biological Neural Network Toolbox] - A free Matlab toolbox for simulating networks of several different types of neurons − *[http://wormweb.org/neuralnet.html WormWeb.org: Interactive Visualization of the C. elegans Neural Network] - C. elegans, a nematode with 302 neurons, is the only organism for whom the entire neural network has been uncovered. Use this site to browse through the network and to search for paths between any 2 neurons. − *[http://nba.uth.tmc.edu/neuroscience/s1/introduction.html Introduction to Neurons and Neuronal Networks], ''Neuroscience Online'' (electronic neuroscience textbook) − *[http://www.gfai.de/~heinz/publications/NI/index.htm Delaying Pulse Networks (Wave Interference Networks)] − 外部链接 − * 脑内神经网络与人工神经网络的比较+ − * 麻省理工学院公开课程讲稿+ − * 大脑中的计算 − * 一种自由的Matlab工具箱，用于模拟几种不同类型的神经元的网络 − * wormweb. org: 线虫神经网络的交互式可视化—— 一种具有302个神经元的线虫，它是唯一一种完整的神经网络被发现的生物。使用这个网站浏览网络，并搜索任何两个神经元之间的路径。 − * 《神经元与神经元网络概论》，''神经科学在线''(神经科学电子教材) − * 延迟脉冲网络(波干扰网络) − {{nervous_system}} − {{Authority control}}+ − + − − − * − Category:Cognition − + + + + + + + + − <noinclude> − <small>This page was moved from [[wikipedia:en:Neural circuit]]. Its edit history can be viewed at [[神经回路/edithistory]]</small></noinclude> − + +
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'''神经回路 neural circuit'''由一群[[神经元]]构成，通过[[突触]]相互连接，被激活时，就可以执行某种特定功能。<ref name="Neuro">{{cite book |last1=Purves |first1=Dale |title=Neuroscience |date=2011 |publisher=Sinauer |location=Sunderland, Mass. |isbn=9780878936953 |page=507 |edition= 5th}}</ref>神经回路彼此再连接，形成[[大尺度脑网络]]。<ref name="CEI">{{cite web |title=Neural Circuits {{!}} Centre of Excellence for Integrative Brain Function |url=https://www.brainfunction.edu.au/research/research-themes/neural-circuits/ |website=Centre of Excellence for Integrative Brain Function |access-date=4 June 2018 |language=en-AU |date=13 June 2016}}</ref>[[生物神经网络]]启发了[[人工神经网络]]的设计，但人工神经网络通常不是机械地复制生物神经网络。
Anatomy of a multipolar neuron
Anatomy of a multipolar neuron
多极神经元的解剖学
多极神经元的解剖学
==Early study==
==早期研究==
[[Image:Cajal actx inter.jpg|thumb|300px|right|From "Texture of the [[Nervous System]] of Man and the [[Vertebrates]]" by [[Santiago Ramón y Cajal]]. The figure illustrates the diversity of neuronal morphologies in the [[auditory cortex]].|链接=Special:FilePath/Cajal_actx_inter.jpg]]
[[Image:Cajal actx inter.jpg|thumb|300px|right|摘自Santiago Ramón y Cajal的《人类和脊椎动物的神经系统结构》。这幅图说明了听觉皮层神经元形态的多样性。]]
对神经网络的早期研究见于赫伯特·斯宾塞 Herbert Spencer的《心理学原理》第三版(1872)、西奥多·梅纳特 Theodor Meynert的《精神病学》(1884)、威廉·詹姆斯 William James的《心理学原理》(1890)和西格蒙德·弗洛伊德 Sigmund Freud的《科学心理学计划》(1895)。<ref name=":0">{{cite web |url=http://psych.stanford.edu/~jlm/papers/ThomasMcCIPCambEncy.pdf |title=Connectionist models of cognition |author1=Michael S. C. Thomas |author2=James L. McClelland |publisher=[[Stanford University]] |access-date=2015-08-31 |archive-url=https://web.archive.org/web/20150906120214/http://psych.stanford.edu/~jlm/papers/ThomasMcCIPCambEncy.pdf |archive-date=2015-09-06 |url-status=dead }}</ref>1949年，赫布 Hebb在其理论（即赫布理论 Hebbian theory</font>）中提出了神经元学习的第一定律。赫布理论认为，配对中突触前神经元和突触后神经元的活动可以充分改变突触连接的动态特性，即要么促进，要么抑制信号传递 signal transmission。1959年，神经科学家, 沃伦·麦卡洛克 Warren Sturgis和沃尔特·皮茨 Walter Pitts发表了关于神经网络处理的第一部著作。<ref name=":1">{{citation | title = What the frog's eye tells the frog's brain. |author1=J. Y. Lettvin |author2=H. R. Maturana |author3=W. S. McCulloch |author4=W. H. Pitts | year = 1959 | work = Proc. Inst. Radio Engr. | issue = 47 | pages = 1940–1951 }}</ref> 他们从理论上证明了人工神经元网络可以实现逻辑、算术和符号功能。生物神经元的简化模型由此建立起来，现在通常被称为感知器或[[人工神经元]]。这些简单的模型解释了神经加成作用(即突触后膜上的电位将在细胞体中加成)。后来的模型也提供了兴奋性和抑制性突触传递。
==神经元之间的连接==
''请见：突触''[[File:Leg Neural Network.jpg|thumb|Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013)]]
''请见：突触''[[File:Leg Neural Network.jpg|thumb|Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013)|链接=Special:FilePath/Leg_Neural_Network.jpg]]
The connections between neurons in the brain are much more complex than those of the [[artificial neuron]]s used in the [[connectionism|connectionist]] neural computing models of [[artificial neural network]]s. The basic kinds of connections between neurons are [[synapse]]s: both [[chemical synapse|chemical]] and [[electrical synapse]]s.
The connections between neurons in the brain are much more complex than those of the [[artificial neuron]]s used in the [[connectionism|connectionist]] neural computing models of [[artificial neural network]]s. The basic kinds of connections between neurons are [[synapse]]s: both [[chemical synapse|chemical]] and [[electrical synapse]]s.
大脑神经元之间的连接比<font color="#ff8000">人工神经元artificial neuron</font>之间的连接要复杂得多，人工神经元常用于人工<font color="#ff8000">神经网络中的连接计算模型connectionist neural computing model</font>。大脑神经元之间的基本连接是<font color="#ff8000">突触synapse</font>,包括：<font color="#ff8000">化学突触chemical synapse</font>和<font color="#ff8000">电突触electrical synapse</font>。
大脑神经元之间的连接比[[人工神经元]]之间的连接要复杂得多，人工神经元常用于人工神经网络中的连接计算模型。大脑神经元之间的基本连接是[[突触]],包括：化学突触和电突触。
文件:Leg Neural Network
文件:Leg Neural Network
理解动作语言的运动语义神经回路组织。灰点代表语言理解的区域，创造了一个理解所有语言的网络。当理解与腿相关的词语时，运动系统的语义回路，特别是腿的运动表征(黄点)被纳入其中。改编自 Shebani 等人。(2013)
理解动作语言的运动语义神经回路组织。灰点代表语言理解的区域，创造了一个理解所有语言的网络。当理解与腿相关的词语时，运动系统的语义回路，特别是腿的运动表征(黄点)被纳入其中。改编自 Shebani 等人。(2013)
突触的建立使得神经元能够连接成千上万个重叠的、相互连接的神经回路。被称为“神经蛋白”的突触前蛋白在这一过程中起着核心作用。<ref name="Sudhof">{{cite journal |last1=Südhof |first1=TC |title=Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits. |journal=Cell |date=2 November 2017 |volume=171 |issue=4 |pages=745–769 |doi=10.1016/j.cell.2017.10.024 |pmid=29100073|pmc=5694349 }}</ref>
神经元工作的其中一个原理是神经加成——[[突触]]后膜上的电位将在细胞体中进行加成。如果[[神经元]]在[[轴突]]处的去极化超过阈值，就会发生动作电位，动作电位沿着轴突向下传递到末端，将信号传递给其他神经元。兴奋性和抑制性突触传递主要通过'''兴奋性突触后电位 exciatory postsynaptic potentials(EPSPs)'''和'''抑制性突触后电位 inhibitory postsynaptic potentials(IPSPs）'''实现。
在电生理层面上，存在着改变个体突触突触可塑性)和个体神经元(内禀可塑性)的反应特征的各种现象。这些可塑性通常分为短期可塑性和长期可塑性。长期突触可塑性通常被认为是最有可能的记忆底物。通常来说，“神经可塑性”指的是由活动或经历引起的大脑变化。
Connections display temporal and spatial characteristics. Temporal characteristics refers to the continuously modified activity-dependent efficacy of synaptic transmission, called [[spike-timing-dependent plasticity]]. It has been observed in several studies that the synaptic efficacy of this transmission can undergo short-term increase (called [[neural facilitation|facilitation]]) or decrease ([[Neural facilitation#Short-term depression|depression]]) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by [[long-term potentiation]] (LTP) or [[long-term depression|depression]] (LTD), depends strongly on the relative timing of the onset of the [[excitatory postsynaptic potential]] and the postsynaptic action potential. LTP is induced by a series of action potentials which cause a variety of biochemical responses. Eventually, the reactions cause the expression of new receptors on the cellular membranes of the postsynaptic neurons or increase the efficacy of the existing receptors through [[phosphorylation]].
Connections display temporal and spatial characteristics. Temporal characteristics refers to the continuously modified activity-dependent efficacy of synaptic transmission, called [[spike-timing-dependent plasticity]]. It has been observed in several studies that the synaptic efficacy of this transmission can undergo short-term increase (called [[neural facilitation|facilitation]]) or decrease ([[Neural facilitation#Short-term depression|depression]]) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by [[long-term potentiation]] (LTP) or [[long-term depression|depression]] (LTD), depends strongly on the relative timing of the onset of the [[excitatory postsynaptic potential]] and the postsynaptic action potential. LTP is induced by a series of action potentials which cause a variety of biochemical responses. Eventually, the reactions cause the expression of new receptors on the cellular membranes of the postsynaptic neurons or increase the efficacy of the existing receptors through [[phosphorylation]].
在某些情况下，当<font color="#ff8000">基底神经节basal ganglia</font>受累时，神经环路会变成病理性的，继而引起问题，如<font color="#ff8000">帕金森病Parkinson's disease</font>。<ref name="French" /> <font color="#ff8000">帕佩兹回路Papez circuit</font>发生问题也会导致包括帕金森氏症在内的一系列<font color="#ff8000">神经退行性疾病neurodegenerative disorders</font>。
在某些情况下，当<font color="#ff8000">基底神经节basal ganglia</font>受累时，神经环路会变成病理性的，继而引起问题，如<font color="#ff8000">帕金森病Parkinson's disease</font>。<ref name="French" /> <font color="#ff8000">帕佩兹回路Papez circuit</font>发生问题也会导致包括帕金森氏症在内的一系列<font color="#ff8000">神经退行性疾病neurodegenerative disorders</font>。
=<nowiki>= 进一步阅读 = =</nowiki>=
==另见==
* 内在可塑性 罗伯特 · H · 卡德莫尔，尼拉杰 · S · 德赛 奖学金百科全书3(2) : 1363. doi: 10.4249/Scholarpedia. 1363
* [[反馈]]
* [[人脑区域分布列表]]
* [[神经编码]]
* [[神经工程]]
* [[神经震荡]]
* [[脉冲耦合神经网络]]
* [[系统神经科学]]
==参考文件==
{{Reflist}}
==进一步阅读==
*[http://www.scholarpedia.org/article/Intrinsic_plasticity Intrinsic plasticity] Robert H. Cudmore, Niraj S. Desai [[Scholarpedia]] 3(2):1363. [[doi:10.4249/scholarpedia.1363]]
[[分类:Neural circuits|*]]
[[Category:Cognition]]
*类别: 认知
==外部链接==
*[https://web.archive.org/web/20040807104201/http://www.his.sunderland.ac.uk/ps/worksh2/denham.pdf 脑内神经网络与人工神经网络的比较]
*[https://web.archive.org/web/20051024152350/http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-95-AResearch-Topics-in-NeuroscienceJanuary--IAP-2003/LectureNotes/ 麻省理工学院公开课程讲稿]
*[http://www.willamette.edu/~gorr/classes/cs449/brain.html 大脑中的计算]
*[http://www.ymer.org/amir/software/biological-neural-networks-toolbox/ Biological Neural Network Toolbox] - 一种自由的Matlab工具箱，用于模拟几种不同类型的神经元的网络
*[http://wormweb.org/neuralnet.html WormWeb.org: 线虫神经网络的交互式可视化]—— 一种具有302个神经元的线虫，它是唯一一种完整的神经网络被发现的生物。使用这个网站浏览网络，并搜索任何两个神经元之间的路径。
*[http://nba.uth.tmc.edu/neuroscience/s1/introduction.html 《神经元与神经元网络概论》，''神经科学在线''(神经科学电子教材)]
*[http://www.gfai.de/~heinz/publications/NI/index.htm 延迟脉冲网络(波干扰网络)]
[[Category:待整理页面]]
[[Category:神经回路]]
[[Category:认知]]