更改

{{short description|Network or circuit of neurons}}

{{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}}

{{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，请见人工神经网络。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，请见神经通路。[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] |链接=Special:FilePath/Blausen_0657_MultipolarNeuron.png]]

[[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.

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.

= = 早期研究 = = 对神经网络的早期研究见于赫伯特 · 斯宾塞的《心理学原理》第三版(1872)、西奥多 · 梅纳特的《精神病学》(1884)、威廉 · 詹姆斯的《心理学原理》(1890)和西格蒙德 · 弗洛伊德的《科学心理学计划》(1895)。1949年，赫布在其理论（即赫布理论）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。1959年，神经科学家，沃伦·麦卡洛克和 Walter Pitts 发表了关于神经网络处理的第一部著作。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。

= = 早期研究 = = 对神经网络的早期研究见于赫伯特 · 斯宾塞的《心理学原理》第三版(1872)、西奥多 · 梅纳特的《精神病学》(1884)、威廉 · 詹姆斯的《心理学原理》(1890)和西格蒙德 · 弗洛伊德的《科学心理学计划》(1895)。1949年，赫布在其理论（即赫布理论）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。1959年，神经科学家，沃伦·麦卡洛克和 Walter Pitts 发表了关于神经网络处理的第一部著作。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。

==Connections between neurons==

==Connections between neurons ==

{{See also|Synapse}}

{{See also|Synapse}}

[[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]]

[[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]]

虽然在发育状态的脑突触中，突触抑制已经被广泛观察到，但据推测，它在成人大脑中改变为易化。

虽然在发育状态的脑突触中，突触抑制已经被广泛观察到，但据推测，它在成人大脑中改变为易化。

==Circuitry==

== Circuitry ==

[[File:Model of Cerebellar Perceptron.jpg|thumb|Model of a neural circuit in the [[cerebellum]]|链接=Special:FilePath/Model_of_Cerebellar_Perceptron.jpg]]

[[File:Model of Cerebellar Perceptron.jpg|thumb|Model of a neural circuit in the [[cerebellum]]|链接=Special:FilePath/Model_of_Cerebellar_Perceptron.jpg]]

An example of a neural circuit is the [[trisynaptic circuit]] in the [[hippocampus]]. Another is the [[Papez circuit]] linking the [[hypothalamus]] to the [[limbic lobe]]. There are several neural circuits in the [[cortico-basal ganglia-thalamo-cortical loop]]. These circuits carry information between the cortex, [[basal ganglia]], thalamus, and back to the cortex. The largest structure within the basal ganglia, the [[striatum]], is seen as having its own internal microcircuitry.<ref name="Stocco">{{cite journal |last1=Stocco |first1=Andrea |last2=Lebiere |first2=Christian |last3=Anderson |first3=John R. |title=Conditional Routing of Information to the Cortex: A Model of the Basal Ganglia's Role in Cognitive Coordination |journal=Psychological Review |volume=117 |issue=2 |pages=541–74 |year=2010 |pmid=20438237 |doi=10.1037/a0019077 |pmc=3064519}}</ref>

An example of a neural circuit is the [[trisynaptic circuit]] in the [[hippocampus]]. Another is the [[Papez circuit]] linking the [[hypothalamus]] to the [[limbic lobe]]. There are several neural circuits in the [[cortico-basal ganglia-thalamo-cortical loop]]. These circuits carry information between the cortex, [[basal ganglia]], thalamus, and back to the cortex. The largest structure within the basal ganglia, the [[striatum]], is seen as having its own internal microcircuitry.<ref name="Stocco">{{cite journal |last1=Stocco |first1=Andrea |last2=Lebiere |first2=Christian |last3=Anderson |first3=John R. |title=Conditional Routing of Information to the Cortex: A Model of the Basal Ganglia's Role in Cognitive Coordination |journal=Psychological Review |volume=117 |issue=2 |pages=541–74 |year=2010 |pmid=20438237 |doi=10.1037/a0019077 |pmc=3064519}}</ref>

In a diverging circuit, one neuron synapses with a number of postsynaptic cells. Each of these

In a diverging circuit, one neuron synapses with a number of postsynaptic cells. Each of these

may synapse with many more making it possible for one neuron to stimulate up to thousands of cells. This is exemplified in the way that thousands of muscle fibers can be stimulated from the initial input from a single [[motor neuron]].<ref name="Saladin"/>

may synapse with many more making it possible for one neuron to stimulate up to thousands of cells. This is exemplified in the way that thousands of muscle fibers can be stimulated from the initial input from a single [[motor neuron]].<ref name="Saladin" />

In a diverging circuit, one neuron synapses with a number of postsynaptic cells. Each of these

In a diverging circuit, one neuron synapses with a number of postsynaptic cells. Each of these

在发散回路中，一个神经元与若干突触后细胞形成突触。这些神经元中的每一个都可能与更多的神经元突触，使一个神经元能够刺激成千上万个细胞。一个运动神经元的初始输入可以刺激数千个肌肉纤维，这就是一个例子。

在发散回路中，一个神经元与若干突触后细胞形成突触。这些神经元中的每一个都可能与更多的神经元突触，使一个神经元能够刺激成千上万个细胞。一个运动神经元的初始输入可以刺激数千个肌肉纤维，这就是一个例子。

In a converging circuit, inputs from many sources are converged into one output, affecting just one neuron or a neuron pool. This type of circuit is exemplified in the [[respiratory center]] of the [[brainstem]], which responds to a number of inputs from different sources by giving out an appropriate breathing pattern.<ref name="Saladin"/>

In a converging circuit, inputs from many sources are converged into one output, affecting just one neuron or a neuron pool. This type of circuit is exemplified in the [[respiratory center]] of the [[brainstem]], which responds to a number of inputs from different sources by giving out an appropriate breathing pattern.<ref name="Saladin" />

In a converging circuit, inputs from many sources are converged into one output, affecting just one neuron or a neuron pool. This type of circuit is exemplified in the respiratory center of the brainstem, which responds to a number of inputs from different sources by giving out an appropriate breathing pattern.

In a converging circuit, inputs from many sources are converged into one output, affecting just one neuron or a neuron pool. This type of circuit is exemplified in the respiratory center of the brainstem, which responds to a number of inputs from different sources by giving out an appropriate breathing pattern.

A reverberating circuit produces a repetitive output. In a signalling procedure from one neuron to another in a linear sequence, one of the neurons may send a signal back to initiating neuron.

A reverberating circuit produces a repetitive output. In a signalling procedure from one neuron to another in a linear sequence, one of the neurons may send a signal back to initiating neuron.

Each time that the first neuron fires, the other neuron further down the sequence fire again sending it back to the source. This restimulates the first neuron and also allows the path of transmission to continue to its output. A resulting repetitive pattern is the outcome that only stops if one or more of the synapses fail, or if an inhibitory feed from another source causes it to stop. This type of reverberating circuit is found in the respiratory center that sends signals to the [[Muscles of respiration|respiratory muscles]], causing inhalation. When the circuit is interrupted by an inhibitory signal the muscles relax causing exhalation. This type of circuit may play a part in [[epileptic seizure]]s.<ref name="Saladin"/>

Each time that the first neuron fires, the other neuron further down the sequence fire again sending it back to the source. This restimulates the first neuron and also allows the path of transmission to continue to its output. A resulting repetitive pattern is the outcome that only stops if one or more of the synapses fail, or if an inhibitory feed from another source causes it to stop. This type of reverberating circuit is found in the respiratory center that sends signals to the [[Muscles of respiration|respiratory muscles]], causing inhalation. When the circuit is interrupted by an inhibitory signal the muscles relax causing exhalation. This type of circuit may play a part in [[epileptic seizure]]s.<ref name="Saladin" />

A reverberating circuit produces a repetitive output. In a signalling procedure from one neuron to another in a linear sequence, one of the neurons may send a signal back to initiating neuron.

A reverberating circuit produces a repetitive output. In a signalling procedure from one neuron to another in a linear sequence, one of the neurons may send a signal back to initiating neuron.

混响电路产生重复的输出。在以线性顺序从一个神经元到另一个神经元的信号传递过程中，其中一个神经元可能会将信号发回初始神经元。每当第一个神经元发出信号时，另一个神经元就会再次发出信号，把信号送回信号源。这将重新刺激第一个神经元，并允许传输路径继续到它的输出。由此产生的重复模式只有在一个或多个突触失效，或来自另一个来源的抑制性馈电导致其停止时才会停止。这种类型的混响电路在呼吸中枢被发现，它向呼吸肌肉发送信号，导致吸入。当回路被抑制信号打断时，肌肉就会放松，导致呼气。这种类型的回路可能在癫痫发作中起作用。

混响电路产生重复的输出。在以线性顺序从一个神经元到另一个神经元的信号传递过程中，其中一个神经元可能会将信号发回初始神经元。每当第一个神经元发出信号时，另一个神经元就会再次发出信号，把信号送回信号源。这将重新刺激第一个神经元，并允许传输路径继续到它的输出。由此产生的重复模式只有在一个或多个突触失效，或来自另一个来源的抑制性馈电导致其停止时才会停止。这种类型的混响电路在呼吸中枢被发现，它向呼吸肌肉发送信号，导致吸入。当回路被抑制信号打断时，肌肉就会放松，导致呼气。这种类型的回路可能在癫痫发作中起作用。

In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec so that the more synapses there are will produce a longer delay to the output neuron. After the input has stopped, the output will go on firing for some time. This type of circuit does not have a feedback loop as does the reverberating circuit. Continued firing after the stimulus has stopped is called ''after-discharge''. This circuit type is found in the [[reflex arc]]s of certain [[reflex]]es.<ref name="Saladin"/>

In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec so that the more synapses there are will produce a longer delay to the output neuron. After the input has stopped, the output will go on firing for some time. This type of circuit does not have a feedback loop as does the reverberating circuit. Continued firing after the stimulus has stopped is called ''after-discharge''. This circuit type is found in the [[reflex arc]]s of certain [[reflex]]es.<ref name="Saladin" />

In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec so that the more synapses there are will produce a longer delay to the output neuron. After the input has stopped, the output will go on firing for some time. This type of circuit does not have a feedback loop as does the reverberating circuit. Continued firing after the stimulus has stopped is called after-discharge. This circuit type is found in the reflex arcs of certain reflexes.

In a parallel after-discharge circuit, a neuron inputs to several chains of neurons. Each chain is made up of a different number of neurons but their signals converge onto one output neuron. Each synapse in the circuit acts to delay the signal by about 0.5 msec so that the more synapses there are will produce a longer delay to the output neuron. After the input has stopped, the output will go on firing for some time. This type of circuit does not have a feedback loop as does the reverberating circuit. Continued firing after the stimulus has stopped is called after-discharge. This circuit type is found in the reflex arcs of certain reflexes.

==See also==

==See also==

* [[Feedback]]

*[[Feedback]]

* [[List of regions in the human brain]]

*[[List of regions in the human brain]]

* [[Network science]]

*[[Network science]]

* [[Neural coding]]

*[[Neural coding]]

* [[Neural engineering]]

*[[Neural engineering]]

* [[Neural oscillation]]

*[[Neural oscillation]]

* [[Pulse-coupled networks]]

*[[Pulse-coupled networks]]

* [[Systems neuroscience]]

*[[Systems neuroscience]]

* Feedback

*Feedback

* List of regions in the human brain

*List of regions in the human brain

* Network science

*Network science

* Neural coding

*Neural coding

* Neural engineering

*Neural engineering

* Neural oscillation

*Neural oscillation

* Pulse-coupled networks

*Pulse-coupled networks

* Systems neuroscience

*Systems neuroscience

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

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

==Further reading==

==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]]

*[http://www.scholarpedia.org/article/Intrinsic_plasticity Intrinsic plasticity] Robert H. Cudmore, Niraj S. Desai [[Scholarpedia]] 3(2):1363. [[doi:10.4249/scholarpedia.1363]]

* Intrinsic plasticity Robert H. Cudmore, Niraj S. Desai Scholarpedia 3(2):1363. doi:10.4249/scholarpedia.1363

*Intrinsic plasticity Robert H. Cudmore, Niraj S. Desai Scholarpedia 3(2):1363. doi:10.4249/scholarpedia.1363

= = 进一步阅读 = = =  

=<nowiki>= 进一步阅读 = =</nowiki>=  

* 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

*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

==External links==

==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/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]

*[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.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://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://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://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)]

*[http://www.gfai.de/~heinz/publications/NI/index.htm Delaying Pulse Networks (Wave Interference Networks)]

* Comparison of Neural Networks in the Brain and Artificial Neural Networks

*Comparison of Neural Networks in the Brain and Artificial Neural Networks

* Lecture notes at MIT OpenCourseWare

*Lecture notes at MIT OpenCourseWare

* Computation in the Brain

*Computation in the Brain

* Biological Neural Network Toolbox - A free Matlab toolbox for simulating networks of several different types of neurons  

*Biological Neural Network Toolbox - A free Matlab toolbox for simulating networks of several different types of neurons

* 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.

* 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.

* Introduction to Neurons and Neuronal Networks, Neuroscience Online (electronic neuroscience textbook)

*Introduction to Neurons and Neuronal Networks, Neuroscience Online (electronic neuroscience textbook)

* Delaying Pulse Networks (Wave Interference Networks)

*Delaying Pulse Networks (Wave Interference Networks)

生物神经网络工具箱-一个免费的 Matlab 工具箱，用于模拟几种不同类型神经元的网络  

生物神经网络工具箱-一个免费的 Matlab 工具箱，用于模拟几种不同类型神经元的网络  

* wormweb. org: 秀丽隐杆线虫神经网络的交互式可视化，一种有302个神经元的线虫，是唯一一种神经网络已经被发现的生物体。使用此网站浏览通过网络和搜索路径之间的任何2个神经元。

*wormweb. org: 秀丽隐杆线虫神经网络的交互式可视化，一种有302个神经元的线虫，是唯一一种神经网络已经被发现的生物体。使用此网站浏览通过网络和搜索路径之间的任何2个神经元。

* 神经元和神经元网络介绍，神经科学在线(电子神经科学教科书)  

*神经元和神经元网络介绍，神经科学在线(电子神经科学教科书)

* 延迟脉冲网络(波干扰网络)

*延迟脉冲网络(波干扰网络)

{{nervous_system}}

{{nervous_system}}

{{Authority control}}

{{Authority control}}

[[Category:Neural circuits|*]]

[[分类:Neural circuits|*]]

[[Category:Cognition]]

[[Category:Cognition]]

Category:Cognition

Category:Cognition

* 类别: 认知

*类别: 认知

<noinclude>

<noinclude>