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Moved page from wikipedia:en:Neural circuit (history)
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{{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}}
[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] ]]
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 neurons interconnected by synapses to carry out a specific function when activated. Neural circuits interconnect to one another to form large scale brain networks. Biological neural networks have inspired the design of artificial neural networks, but artificial neural networks are usually not strict copies of their biological counterparts.
神经回路是由突触相互连接的神经元群体，当被激活时，这些神经元群体执行特定的功能。神经回路相互连接，形成大规模的大脑网络。生物神经网络已经启发了人工神经网络的设计，但人工神经网络通常不是严格的复制他们的生物对应。
==Early study==
[[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]].]]
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>{{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>{{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.
Early treatments of neural 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). The first rule of neuronal learning was described by 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 signal transmission. In 1959, the neuroscientists, Warren Sturgis McCulloch and Walter Pitts published the first works on the processing of neural networks. They showed theoretically that networks of artificial neurons could implement logical, arithmetic, and symbolic functions. Simplified models of biological neurons were set up, now usually called perceptrons or artificial neurons. These simple models accounted for 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 = = 对神经网络的早期处理见于赫伯特 · 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，Hebb 在 Hebbian 理论中提出了神经元学习的第一规则。因此，Hebbian 配对突触前和突触后活动可以充分改变突触连接的动态特性，因此要么促进或抑制信号传递。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被建立起来，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的总和(即突触后膜上的电位将在细胞体中总和)。后来的模型也提供了兴奋性和抑制性突触传递。
==Connections between neurons==
{{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)]]
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.
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)
The connections between neurons in the brain are much more complex than those of the artificial neurons used in the connectionist neural computing models of artificial neural networks. The basic kinds of connections between neurons are synapses: both chemical and electrical synapses.
= = 神经元之间的连接 = = 拇指 | 动作语言理解的运动语义神经回路的组织。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，运动系统的语义电路，特别是腿的运动表征(黄点)被纳入其中。改编自 Shebani 等人。(2013年)大脑神经元之间的连接比人工神经网络的连接主义神经计算模型中使用的人工神经元要复杂得多。神经元之间的基本连接是突触: 包括化学突触和电突触。
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>
The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called neurexins are central to this process.
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。称为 neurexins 的突触前蛋白质是这个过程的中心。
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).
One principle by which neurons work is neural summation – potentials at the 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).
神经元工作的一个原理是在突触后膜上的神经求和电位将在细胞体内得到总结。如果轴突柄处的神经元去极化超过阈值，就会产生动作电位，沿轴突向下传递到末端末梢，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(epsp)和抑制性突触后电位(ipsp)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.
On the 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.
在电生理层面，有各种现象改变个体突触(称为突触可塑性)和个体神经元的反应特性(内在可塑性)。这些常常分为短期可塑性和长期可塑性。长期突触可塑性是最有可能的记忆基质。通常，术语“神经可塑性”指的是由活动或经验引起的大脑变化。
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 facilitation) or decrease (depression) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by long-term potentiation (LTP) or 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.
连接具有时间和空间特征。时间特征是指不断修改的活动依赖功效的突触传递，所谓的电峰时间相关突触可塑性。一些研究已经观察到，这种传递的突触功效可以经历短期的增加(称为易化)或减少(抑郁)根据突触前神经元的活动。长时程增强作用或抑制剂诱导突触效能的长期变化，很大程度上取决于兴奋性突触后电位和突触后动作电位发生的相对时间。LTP 是由一系列动作电位引起的，动作电位引起多种生化反应。最终，这些反应导致新受体在突触后神经元的细胞膜上表达，或者通过磷酸化增加现有受体的功效。
Backpropagating action potentials cannot occur because after an action potential travels down a given segment of the axon, the [[Depolarizing pre-pulse#Hodgkin–Huxley model|m gate]]s on [[voltage-gated sodium channel]]s close, thus blocking any transient opening of the [[Depolarizing pre-pulse#Hodgkin–Huxley model|h gate]] from causing a change in the intracellular sodium ion (Na<sup>+</sup>) concentration, and preventing the generation of an action potential back towards the cell body. In some cells, however, [[neural backpropagation]] does occur through the [[dendrite|dendritic branching]] and may have important effects on synaptic plasticity and computation.
Backpropagating action potentials cannot occur because after an action potential travels down a given segment of the axon, the m gates on voltage-gated sodium channels close, thus blocking any transient opening of the h gate from causing a change in the intracellular sodium ion (Na+) concentration, and preventing the generation of an action potential back towards the cell body. In some cells, however, neural backpropagation does occur through the dendritic branching and may have important effects on synaptic plasticity and computation.
背向传导动作电位不能发生，因为在动作电位下行到轴突的某一特定部分后，电压门控钠通道上的 m 门关闭，从而阻止 h 门的任何瞬间开启引起细胞内钠离子(Na +)浓度的变化，并阻止动作电位回到细胞体内。然而，在一些细胞中，神经反向传播通过树突的分支发生，并可能对突触可塑性和计算产生重要影响。
A neuron in the brain requires a single signal to a [[neuromuscular junction]] to stimulate contraction of the postsynaptic muscle cell. In the spinal cord, however, at least 75 [[afferent nerve|afferent]] neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their [[Excitatory postsynaptic potential|EPSPs]] over a wider period of time than others.
A neuron in the brain requires a single signal to a neuromuscular junction to stimulate contraction of the postsynaptic muscle cell. In the spinal cord, however, at least 75 afferent neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their EPSPs over a wider period of time than others.
大脑中的一个神经元需要一个单一的信号到一个神经肌肉接点来刺激突触后肌肉细胞的收缩。然而，在脊髓中，至少需要75个传入神经元来产生放电。神经元之间时间常数的变化更加复杂了，因为一些细胞可以比其他细胞经历更长的时间。
While in synapses in the [[development of the human brain|developing brain]] synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains.
While in synapses in the developing brain synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains.
在发育中的大脑突触中，突触抑制已经被广泛观察到，有人推测它在成人大脑中会发生易化变化。
==Circuitry==
[[File:Model of Cerebellar Perceptron.jpg|thumb|Model of a neural circuit in the [[cerebellum]]]]
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.
海马体的三突触回路就是神经回路的一个例子。另一个是连接下丘脑和边缘叶的 Papez 电路。在皮质-基底节-丘脑-皮质环中存在多个神经回路。这些神经回路在皮层、基底神经节、丘脑之间传递信息，并回到大脑皮层。在基底神经节中最大的结构，纹状体，被认为有自己的内部微电路。
Neural circuits in the [[spinal cord]] called [[central pattern generator]]s are responsible for controlling motor instructions involved in rhythmic behaviours. Rhythmic behaviours include walking, [[urination]], and [[ejaculation]]. The central pattern generators are made up of different groups of [[spinal interneuron]]s.<ref name="Guertin">{{cite journal |last1=Guertin |first1=PA |title=Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. |journal=Frontiers in Neurology |date=2012 |volume=3 |pages=183 |doi=10.3389/fneur.2012.00183 |pmid=23403923|pmc=3567435 }}</ref>
Neural circuits in the spinal cord called central pattern generators are responsible for controlling motor instructions involved in rhythmic behaviours. Rhythmic behaviours include walking, urination, and ejaculation. The central pattern generators are made up of different groups of spinal interneurons.
脊髓中的神经回路称为中央模式发生器，负责控制涉及节律行为的运动指令。有节奏的行为包括步行、排尿和射精。中枢模式发生器由不同的脊髓中间神经元群组成。
There are four principal types of neural circuits that are responsible for a broad scope of neural functions. These circuits are a '''diverging circuit''', a '''converging circuit''', a '''reverberating circuit''', and a '''parallel after-discharge circuit'''.<ref name="Saladin">{{cite book |last1=Saladin |first1=K |title=Human anatomy |publisher=McGraw-Hill |isbn=9780071222075 |page=364 |edition=3rd}}</ref>
There are four principal types of neural circuits that are responsible for a broad scope of neural functions. These circuits are a diverging circuit, a converging circuit, a reverberating circuit, and a parallel after-discharge circuit.
有四种主要类型的神经回路负责广泛的神经功能。这些电路包括发散电路、会聚电路、混响电路和并联后放电电路。
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"/>
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.
在发散回路中，一个神经元与许多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形成突触，从而使一个神经元刺激多达数千个细胞成为可能。例如，从单个运动神经元的初始输入可以刺激到成千上万的肌纤维。
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.
在会聚电路中，来自多个源的输入收敛到一个输出，只影响一个神经元或一个神经元池。脑干的呼吸中枢就是这种类型的电路的例子，它通过发出适当的呼吸模式来响应来自不同来源的大量输入信号。
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"/>
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 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 seizures.
混响电路产生重复输出。在以线性序列从一个神经元到另一个神经元的信号传递过程中，其中一个神经元可以将信号发送回起始神经元。每当第一个神经元触发时，另一个神经元沿着序列触发的方向向下再次将其发送回源。这就重新调节了第一个神经元，也使得传递的路径继续到它的输出。由此产生的重复模式是只有当一个或多个突触失效，或者来自其他来源的抑制信号导致突触停止时才会停止的结果。这种反射回路位于呼吸中枢，向呼吸肌发送信号，引起吸入。当电路被抑制信号中断时，肌肉松弛导致呼气。这种类型的电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 arcs of certain reflexes.
在并联的后放电回路中，一个神经元输入到几个神经元链。每个链由不同数量的神经元组成，但是它们的信号会聚到一个输出神经元上。电路中的每一个突触都会将信号延迟0.5毫秒左右，因此，突触越多，输出神经元的延迟时间就越长。在输入停止之后，输出将持续一段时间。这种类型的电路不像混响电路那样有反馈回路。刺激停止后的持续放电称为后放电。这种回路类型存在于某些反射的反射弧中。
==Study methods==
{{See also|Neuropsychology|Cognitive neuropsychology}}
Different [[neuroimaging]] techniques have been developed to investigate the activity of neural circuits and networks. The use of "brain scanners" or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high-resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include [[functional magnetic resonance imaging]] (fMRI), [[brain positron emission tomography]] (brain PET), and [[computed axial tomography]] (CAT) scans. [[Functional neuroimaging]] uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures [[hemodynamic response|hemodynamic activity]] (using [[Blood-oxygen-level dependent imaging|BOLD-contrast imaging]]) which is closely linked to neural activity, PET, and [[electroencephalography]] (EEG) is used.
Different neuroimaging techniques have been developed to investigate the activity of neural circuits and networks. The use of "brain scanners" or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high-resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include functional magnetic resonance imaging (fMRI), brain positron emission tomography (brain PET), and computed axial tomography (CAT) scans. Functional neuroimaging uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures hemodynamic activity (using BOLD-contrast imaging) which is closely linked to neural activity, PET, and electroencephalography (EEG) is used.
为了研究神经回路和神经网络的活动，人们开发了不同的神经成像技术。使用“大脑扫描仪”或功能神经影像来研究大脑的结构或功能是很常见的，要么仅仅作为一种通过高分辨率图像更好地评估大脑损伤的方法，要么通过检查不同大脑区域的相对激活。这些技术可能包括功能性磁共振成像扫描(fMRI)、大脑正电子发射计算机断层扫描扫描(PET)和计算机轴向断层扫描(CAT)。功能神经影像使用特定的大脑成像技术对大脑进行扫描，通常是当一个人正在做一个特定的任务时，试图了解特定大脑区域的激活是如何与任务相关的。在功能神经影像，特别是功能性磁共振成像，它测量血液动力学活动(使用 bold 对比成像) ，这是紧密相连的神经活动，PET，和脑电图(EEG)被使用。
[[Connectionism|Connectionist]] models serve as a test platform for different hypotheses of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. [[artificial neural network]]s, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of [[Parkinson's disease|Parkinson's]] patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process iterative.
Connectionist models serve as a test platform for different hypotheses of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. artificial neural networks, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of Parkinson's patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process iterative.
连接主义模型为不同的表征、信息处理和信号传输假设提供了一个测试平台。在这些模型中进行的损伤研究，例如:。人工神经网络，其中部分节点被故意破坏，以观察网络的表现，也可以产生重要的见解，在工作的几个细胞组装。同样，模拟神经系统疾病中功能失调的神经递质(例如，帕金森病人基底神经节中的多巴胺) ，可以深入了解在特定患者群体中观察到的认知缺陷模式的潜在机制。来自这些模型的预测可以在患者身上或通过药理操作进行测试，而这些研究反过来可以用来告知模型，使过程迭代。
The modern balance between the connectionist approach and the single-cell approach in [[neurobiology]] has been achieved through a lengthy discussion.
In 1972, Barlow announced the ''single neuron revolution'': "our perceptions are caused by the activity of a rather small number of neurons selected from a very large population of predominantly silent cells."<ref name="Barlow1972">{{cite journal |last1=Barlow|first1=HB|title=Single units and sensation: a neuron doctrine for perceptual psychology? |journal=Perception|date=December 1, 1972|volume=1|issue=4 |pages=371-394|doi=10.1068/p010371|pmid=4377168}}</ref> This approach was stimulated by the idea of [[grandmother cell]] put forward two years earlier. Barlow formulated "five dogmas" of neuron doctrine. Recent studies of '[[grandmother cell]]' and sparse coding phenomena develop and modify these ideas.<ref name="QuianQuiroga2005">{{cite journal |last1=Quian Quiroga|first1=R|last2=Reddy|first2=L|last3=Kreiman|first3=G|last4=Koch|first4=C|last5=Fried|first5=I|title=Invariant visual representation by single neurons in the human brain|journal=Nature|date=Jun 23, 2005|volume=435|issue=7045|pages=1102-1107|doi=10.1038/nature03687|doi-access=free|pmid=15973409}}</ref> The single cell experiments used intracranial electrodes in the medial temporal lobe (the hippocampus and surrounding cortex). Modern development of [[concentration of measure]] theory (stochastic separation theorems) with applications to [[artificial neural networks]] give mathematical background to unexpected effectiveness of small neural ensembles in high-dimensional brain.<ref>{{cite journal |last1= Gorban|first1= Alexander N.|last2= Makarov|first2= Valeri A.|last3= Tyukin |first3= Ivan Y.|date= July 2019|title= The unreasonable effectiveness of small neural ensembles in high-dimensional brain|journal= Physics of Life Reviews|volume= 29 |pages= 55–88|doi= 10.1016/j.plrev.2018.09.005|doi-access=free|pmid= 30366739|arxiv= 1809.07656}}</ref>
The modern balance between the connectionist approach and the single-cell approach in neurobiology has been achieved through a lengthy discussion.
In 1972, Barlow announced the single neuron revolution: "our perceptions are caused by the activity of a rather small number of neurons selected from a very large population of predominantly silent cells." This approach was stimulated by the idea of grandmother cell put forward two years earlier. Barlow formulated "five dogmas" of neuron doctrine. Recent studies of 'grandmother cell' and sparse coding phenomena develop and modify these ideas. The single cell experiments used intracranial electrodes in the medial temporal lobe (the hippocampus and surrounding cortex). Modern development of concentration of measure theory (stochastic separation theorems) with applications to artificial neural networks give mathematical background to unexpected effectiveness of small neural ensembles in high-dimensional brain.
神经生物学中的连接主义方法和单细胞方法之间的现代平衡已经通过长时间的讨论达到。1972年，巴洛宣布了单个神经元的革命: “我们的感知是由一个相当少的神经元的活动引起的，这些神经元是从一个非常庞大的主要无声细胞群中挑选出来的。”这种方法是由两年前提出的祖母细胞的想法刺激的。巴洛提出了神经元学说的“五教条”。最近的研究“祖母细胞”和稀疏编码现象发展和修改这些想法。单细胞实验在内侧颞叶(海马和周围皮质)使用颅内电极。测度集中理论(随机分离定理)的现代发展及其在人工神经网络中的应用，为高维大脑中小型神经系统的意想不到的有效性提供了数学背景。
==Clinical significance==
Sometimes neural circuitries can become pathological and cause problems such as in [[Parkinson's disease]] when the [[basal ganglia]] are involved.<ref name="French">{{cite journal |last1=French |first1=IT |last2=Muthusamy |first2=KA |title=A Review of the Pedunculopontine Nucleus in Parkinson's Disease. |journal=Frontiers in Aging Neuroscience |date=2018 |volume=10 |pages=99 |doi=10.3389/fnagi.2018.00099 |pmid=29755338|pmc=5933166 }}</ref> Problems in the [[Papez circuit]] can also give rise to a number of [[neurodegeneration|neurodegenerative disorders]] including Parkinson's.
Sometimes neural circuitries can become pathological and cause problems such as in Parkinson's disease when the basal ganglia are involved. Problems in the Papez circuit can also give rise to a number of neurodegenerative disorders including Parkinson's.
临床意义有时候，当基底神经节受累时，神经环路可以变成病理性的，并引起问题，如帕金森病。Papez 神经回路的问题也会引起一些神经退行性疾病，包括帕金森氏症。
==See also==
* [[Feedback]]
* [[List of regions in the human brain]]
* [[Network science]]
* [[Neural coding]]
* [[Neural engineering]]
* [[Neural oscillation]]
* [[Pulse-coupled networks]]
* [[Systems neuroscience]]
* Feedback
* List of regions in the human brain
* Network science
* Neural coding
* Neural engineering
* Neural oscillation
* Pulse-coupled networks
* 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
==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]]
* Intrinsic plasticity Robert H. Cudmore, Niraj S. Desai Scholarpedia 3(2):1363. doi:10.4249/scholarpedia.1363
= = 进一步阅读 = = =
* 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==
* [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)]
* Comparison of Neural Networks in the Brain and Artificial Neural Networks
* Lecture notes at MIT OpenCourseWare
* Computation in the Brain
* 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.
* Introduction to Neurons and Neuronal Networks, Neuroscience Online (electronic neuroscience textbook)
* 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
* 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)
* 延迟脉冲网络(波干扰网络)
{{nervous_system}}
{{Authority control}}
[[Category:Neural circuits|*]]
[[Category:Cognition]]
*
Category:Cognition
* 类别: 认知
<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>
[[Category:待整理页面]]
{{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}}
[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] ]]
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 neurons interconnected by synapses to carry out a specific function when activated. Neural circuits interconnect to one another to form large scale brain networks. Biological neural networks have inspired the design of artificial neural networks, but artificial neural networks are usually not strict copies of their biological counterparts.
神经回路是由突触相互连接的神经元群体，当被激活时，这些神经元群体执行特定的功能。神经回路相互连接，形成大规模的大脑网络。生物神经网络已经启发了人工神经网络的设计，但人工神经网络通常不是严格的复制他们的生物对应。
==Early study==
[[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]].]]
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>{{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>{{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.
Early treatments of neural 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). The first rule of neuronal learning was described by 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 signal transmission. In 1959, the neuroscientists, Warren Sturgis McCulloch and Walter Pitts published the first works on the processing of neural networks. They showed theoretically that networks of artificial neurons could implement logical, arithmetic, and symbolic functions. Simplified models of biological neurons were set up, now usually called perceptrons or artificial neurons. These simple models accounted for 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 = = 对神经网络的早期处理见于赫伯特 · 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，Hebb 在 Hebbian 理论中提出了神经元学习的第一规则。因此，Hebbian 配对突触前和突触后活动可以充分改变突触连接的动态特性，因此要么促进或抑制信号传递。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被建立起来，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的总和(即突触后膜上的电位将在细胞体中总和)。后来的模型也提供了兴奋性和抑制性突触传递。
==Connections between neurons==
{{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)]]
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.
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)
The connections between neurons in the brain are much more complex than those of the artificial neurons used in the connectionist neural computing models of artificial neural networks. The basic kinds of connections between neurons are synapses: both chemical and electrical synapses.
= = 神经元之间的连接 = = 拇指 | 动作语言理解的运动语义神经回路的组织。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，运动系统的语义电路，特别是腿的运动表征(黄点)被纳入其中。改编自 Shebani 等人。(2013年)大脑神经元之间的连接比人工神经网络的连接主义神经计算模型中使用的人工神经元要复杂得多。神经元之间的基本连接是突触: 包括化学突触和电突触。
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>
The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called neurexins are central to this process.
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。称为 neurexins 的突触前蛋白质是这个过程的中心。
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).
One principle by which neurons work is neural summation – potentials at the 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).
神经元工作的一个原理是在突触后膜上的神经求和电位将在细胞体内得到总结。如果轴突柄处的神经元去极化超过阈值，就会产生动作电位，沿轴突向下传递到末端末梢，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(epsp)和抑制性突触后电位(ipsp)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.
On the 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.
在电生理层面，有各种现象改变个体突触(称为突触可塑性)和个体神经元的反应特性(内在可塑性)。这些常常分为短期可塑性和长期可塑性。长期突触可塑性是最有可能的记忆基质。通常，术语“神经可塑性”指的是由活动或经验引起的大脑变化。
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 facilitation) or decrease (depression) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by long-term potentiation (LTP) or 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.
连接具有时间和空间特征。时间特征是指不断修改的活动依赖功效的突触传递，所谓的电峰时间相关突触可塑性。一些研究已经观察到，这种传递的突触功效可以经历短期的增加(称为易化)或减少(抑郁)根据突触前神经元的活动。长时程增强作用或抑制剂诱导突触效能的长期变化，很大程度上取决于兴奋性突触后电位和突触后动作电位发生的相对时间。LTP 是由一系列动作电位引起的，动作电位引起多种生化反应。最终，这些反应导致新受体在突触后神经元的细胞膜上表达，或者通过磷酸化增加现有受体的功效。
Backpropagating action potentials cannot occur because after an action potential travels down a given segment of the axon, the [[Depolarizing pre-pulse#Hodgkin–Huxley model|m gate]]s on [[voltage-gated sodium channel]]s close, thus blocking any transient opening of the [[Depolarizing pre-pulse#Hodgkin–Huxley model|h gate]] from causing a change in the intracellular sodium ion (Na<sup>+</sup>) concentration, and preventing the generation of an action potential back towards the cell body. In some cells, however, [[neural backpropagation]] does occur through the [[dendrite|dendritic branching]] and may have important effects on synaptic plasticity and computation.
Backpropagating action potentials cannot occur because after an action potential travels down a given segment of the axon, the m gates on voltage-gated sodium channels close, thus blocking any transient opening of the h gate from causing a change in the intracellular sodium ion (Na+) concentration, and preventing the generation of an action potential back towards the cell body. In some cells, however, neural backpropagation does occur through the dendritic branching and may have important effects on synaptic plasticity and computation.
背向传导动作电位不能发生，因为在动作电位下行到轴突的某一特定部分后，电压门控钠通道上的 m 门关闭，从而阻止 h 门的任何瞬间开启引起细胞内钠离子(Na +)浓度的变化，并阻止动作电位回到细胞体内。然而，在一些细胞中，神经反向传播通过树突的分支发生，并可能对突触可塑性和计算产生重要影响。
A neuron in the brain requires a single signal to a [[neuromuscular junction]] to stimulate contraction of the postsynaptic muscle cell. In the spinal cord, however, at least 75 [[afferent nerve|afferent]] neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their [[Excitatory postsynaptic potential|EPSPs]] over a wider period of time than others.
A neuron in the brain requires a single signal to a neuromuscular junction to stimulate contraction of the postsynaptic muscle cell. In the spinal cord, however, at least 75 afferent neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their EPSPs over a wider period of time than others.
大脑中的一个神经元需要一个单一的信号到一个神经肌肉接点来刺激突触后肌肉细胞的收缩。然而，在脊髓中，至少需要75个传入神经元来产生放电。神经元之间时间常数的变化更加复杂了，因为一些细胞可以比其他细胞经历更长的时间。
While in synapses in the [[development of the human brain|developing brain]] synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains.
While in synapses in the developing brain synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains.
在发育中的大脑突触中，突触抑制已经被广泛观察到，有人推测它在成人大脑中会发生易化变化。
==Circuitry==
[[File:Model of Cerebellar Perceptron.jpg|thumb|Model of a neural circuit in the [[cerebellum]]]]
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.
海马体的三突触回路就是神经回路的一个例子。另一个是连接下丘脑和边缘叶的 Papez 电路。在皮质-基底节-丘脑-皮质环中存在多个神经回路。这些神经回路在皮层、基底神经节、丘脑之间传递信息，并回到大脑皮层。在基底神经节中最大的结构，纹状体，被认为有自己的内部微电路。
Neural circuits in the [[spinal cord]] called [[central pattern generator]]s are responsible for controlling motor instructions involved in rhythmic behaviours. Rhythmic behaviours include walking, [[urination]], and [[ejaculation]]. The central pattern generators are made up of different groups of [[spinal interneuron]]s.<ref name="Guertin">{{cite journal |last1=Guertin |first1=PA |title=Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. |journal=Frontiers in Neurology |date=2012 |volume=3 |pages=183 |doi=10.3389/fneur.2012.00183 |pmid=23403923|pmc=3567435 }}</ref>
Neural circuits in the spinal cord called central pattern generators are responsible for controlling motor instructions involved in rhythmic behaviours. Rhythmic behaviours include walking, urination, and ejaculation. The central pattern generators are made up of different groups of spinal interneurons.
脊髓中的神经回路称为中央模式发生器，负责控制涉及节律行为的运动指令。有节奏的行为包括步行、排尿和射精。中枢模式发生器由不同的脊髓中间神经元群组成。
There are four principal types of neural circuits that are responsible for a broad scope of neural functions. These circuits are a '''diverging circuit''', a '''converging circuit''', a '''reverberating circuit''', and a '''parallel after-discharge circuit'''.<ref name="Saladin">{{cite book |last1=Saladin |first1=K |title=Human anatomy |publisher=McGraw-Hill |isbn=9780071222075 |page=364 |edition=3rd}}</ref>
There are four principal types of neural circuits that are responsible for a broad scope of neural functions. These circuits are a diverging circuit, a converging circuit, a reverberating circuit, and a parallel after-discharge circuit.
有四种主要类型的神经回路负责广泛的神经功能。这些电路包括发散电路、会聚电路、混响电路和并联后放电电路。
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"/>
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.
在发散回路中，一个神经元与许多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形成突触，从而使一个神经元刺激多达数千个细胞成为可能。例如，从单个运动神经元的初始输入可以刺激到成千上万的肌纤维。
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.
在会聚电路中，来自多个源的输入收敛到一个输出，只影响一个神经元或一个神经元池。脑干的呼吸中枢就是这种类型的电路的例子，它通过发出适当的呼吸模式来响应来自不同来源的大量输入信号。
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"/>
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 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 seizures.
混响电路产生重复输出。在以线性序列从一个神经元到另一个神经元的信号传递过程中，其中一个神经元可以将信号发送回起始神经元。每当第一个神经元触发时，另一个神经元沿着序列触发的方向向下再次将其发送回源。这就重新调节了第一个神经元，也使得传递的路径继续到它的输出。由此产生的重复模式是只有当一个或多个突触失效，或者来自其他来源的抑制信号导致突触停止时才会停止的结果。这种反射回路位于呼吸中枢，向呼吸肌发送信号，引起吸入。当电路被抑制信号中断时，肌肉松弛导致呼气。这种类型的电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 arcs of certain reflexes.
在并联的后放电回路中，一个神经元输入到几个神经元链。每个链由不同数量的神经元组成，但是它们的信号会聚到一个输出神经元上。电路中的每一个突触都会将信号延迟0.5毫秒左右，因此，突触越多，输出神经元的延迟时间就越长。在输入停止之后，输出将持续一段时间。这种类型的电路不像混响电路那样有反馈回路。刺激停止后的持续放电称为后放电。这种回路类型存在于某些反射的反射弧中。
==Study methods==
{{See also|Neuropsychology|Cognitive neuropsychology}}
Different [[neuroimaging]] techniques have been developed to investigate the activity of neural circuits and networks. The use of "brain scanners" or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high-resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include [[functional magnetic resonance imaging]] (fMRI), [[brain positron emission tomography]] (brain PET), and [[computed axial tomography]] (CAT) scans. [[Functional neuroimaging]] uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures [[hemodynamic response|hemodynamic activity]] (using [[Blood-oxygen-level dependent imaging|BOLD-contrast imaging]]) which is closely linked to neural activity, PET, and [[electroencephalography]] (EEG) is used.
Different neuroimaging techniques have been developed to investigate the activity of neural circuits and networks. The use of "brain scanners" or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high-resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include functional magnetic resonance imaging (fMRI), brain positron emission tomography (brain PET), and computed axial tomography (CAT) scans. Functional neuroimaging uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures hemodynamic activity (using BOLD-contrast imaging) which is closely linked to neural activity, PET, and electroencephalography (EEG) is used.
为了研究神经回路和神经网络的活动，人们开发了不同的神经成像技术。使用“大脑扫描仪”或功能神经影像来研究大脑的结构或功能是很常见的，要么仅仅作为一种通过高分辨率图像更好地评估大脑损伤的方法，要么通过检查不同大脑区域的相对激活。这些技术可能包括功能性磁共振成像扫描(fMRI)、大脑正电子发射计算机断层扫描扫描(PET)和计算机轴向断层扫描(CAT)。功能神经影像使用特定的大脑成像技术对大脑进行扫描，通常是当一个人正在做一个特定的任务时，试图了解特定大脑区域的激活是如何与任务相关的。在功能神经影像，特别是功能性磁共振成像，它测量血液动力学活动(使用 bold 对比成像) ，这是紧密相连的神经活动，PET，和脑电图(EEG)被使用。
[[Connectionism|Connectionist]] models serve as a test platform for different hypotheses of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. [[artificial neural network]]s, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of [[Parkinson's disease|Parkinson's]] patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process iterative.
Connectionist models serve as a test platform for different hypotheses of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. artificial neural networks, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of Parkinson's patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process iterative.
连接主义模型为不同的表征、信息处理和信号传输假设提供了一个测试平台。在这些模型中进行的损伤研究，例如:。人工神经网络，其中部分节点被故意破坏，以观察网络的表现，也可以产生重要的见解，在工作的几个细胞组装。同样，模拟神经系统疾病中功能失调的神经递质(例如，帕金森病人基底神经节中的多巴胺) ，可以深入了解在特定患者群体中观察到的认知缺陷模式的潜在机制。来自这些模型的预测可以在患者身上或通过药理操作进行测试，而这些研究反过来可以用来告知模型，使过程迭代。
The modern balance between the connectionist approach and the single-cell approach in [[neurobiology]] has been achieved through a lengthy discussion.
In 1972, Barlow announced the ''single neuron revolution'': "our perceptions are caused by the activity of a rather small number of neurons selected from a very large population of predominantly silent cells."<ref name="Barlow1972">{{cite journal |last1=Barlow|first1=HB|title=Single units and sensation: a neuron doctrine for perceptual psychology? |journal=Perception|date=December 1, 1972|volume=1|issue=4 |pages=371-394|doi=10.1068/p010371|pmid=4377168}}</ref> This approach was stimulated by the idea of [[grandmother cell]] put forward two years earlier. Barlow formulated "five dogmas" of neuron doctrine. Recent studies of '[[grandmother cell]]' and sparse coding phenomena develop and modify these ideas.<ref name="QuianQuiroga2005">{{cite journal |last1=Quian Quiroga|first1=R|last2=Reddy|first2=L|last3=Kreiman|first3=G|last4=Koch|first4=C|last5=Fried|first5=I|title=Invariant visual representation by single neurons in the human brain|journal=Nature|date=Jun 23, 2005|volume=435|issue=7045|pages=1102-1107|doi=10.1038/nature03687|doi-access=free|pmid=15973409}}</ref> The single cell experiments used intracranial electrodes in the medial temporal lobe (the hippocampus and surrounding cortex). Modern development of [[concentration of measure]] theory (stochastic separation theorems) with applications to [[artificial neural networks]] give mathematical background to unexpected effectiveness of small neural ensembles in high-dimensional brain.<ref>{{cite journal |last1= Gorban|first1= Alexander N.|last2= Makarov|first2= Valeri A.|last3= Tyukin |first3= Ivan Y.|date= July 2019|title= The unreasonable effectiveness of small neural ensembles in high-dimensional brain|journal= Physics of Life Reviews|volume= 29 |pages= 55–88|doi= 10.1016/j.plrev.2018.09.005|doi-access=free|pmid= 30366739|arxiv= 1809.07656}}</ref>
The modern balance between the connectionist approach and the single-cell approach in neurobiology has been achieved through a lengthy discussion.
In 1972, Barlow announced the single neuron revolution: "our perceptions are caused by the activity of a rather small number of neurons selected from a very large population of predominantly silent cells." This approach was stimulated by the idea of grandmother cell put forward two years earlier. Barlow formulated "five dogmas" of neuron doctrine. Recent studies of 'grandmother cell' and sparse coding phenomena develop and modify these ideas. The single cell experiments used intracranial electrodes in the medial temporal lobe (the hippocampus and surrounding cortex). Modern development of concentration of measure theory (stochastic separation theorems) with applications to artificial neural networks give mathematical background to unexpected effectiveness of small neural ensembles in high-dimensional brain.
神经生物学中的连接主义方法和单细胞方法之间的现代平衡已经通过长时间的讨论达到。1972年，巴洛宣布了单个神经元的革命: “我们的感知是由一个相当少的神经元的活动引起的，这些神经元是从一个非常庞大的主要无声细胞群中挑选出来的。”这种方法是由两年前提出的祖母细胞的想法刺激的。巴洛提出了神经元学说的“五教条”。最近的研究“祖母细胞”和稀疏编码现象发展和修改这些想法。单细胞实验在内侧颞叶(海马和周围皮质)使用颅内电极。测度集中理论(随机分离定理)的现代发展及其在人工神经网络中的应用，为高维大脑中小型神经系统的意想不到的有效性提供了数学背景。
==Clinical significance==
Sometimes neural circuitries can become pathological and cause problems such as in [[Parkinson's disease]] when the [[basal ganglia]] are involved.<ref name="French">{{cite journal |last1=French |first1=IT |last2=Muthusamy |first2=KA |title=A Review of the Pedunculopontine Nucleus in Parkinson's Disease. |journal=Frontiers in Aging Neuroscience |date=2018 |volume=10 |pages=99 |doi=10.3389/fnagi.2018.00099 |pmid=29755338|pmc=5933166 }}</ref> Problems in the [[Papez circuit]] can also give rise to a number of [[neurodegeneration|neurodegenerative disorders]] including Parkinson's.
Sometimes neural circuitries can become pathological and cause problems such as in Parkinson's disease when the basal ganglia are involved. Problems in the Papez circuit can also give rise to a number of neurodegenerative disorders including Parkinson's.
临床意义有时候，当基底神经节受累时，神经环路可以变成病理性的，并引起问题，如帕金森病。Papez 神经回路的问题也会引起一些神经退行性疾病，包括帕金森氏症。
==See also==
* [[Feedback]]
* [[List of regions in the human brain]]
* [[Network science]]
* [[Neural coding]]
* [[Neural engineering]]
* [[Neural oscillation]]
* [[Pulse-coupled networks]]
* [[Systems neuroscience]]
* Feedback
* List of regions in the human brain
* Network science
* Neural coding
* Neural engineering
* Neural oscillation
* Pulse-coupled networks
* 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
==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]]
* Intrinsic plasticity Robert H. Cudmore, Niraj S. Desai Scholarpedia 3(2):1363. doi:10.4249/scholarpedia.1363
= = 进一步阅读 = = =
* 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==
* [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)]
* Comparison of Neural Networks in the Brain and Artificial Neural Networks
* Lecture notes at MIT OpenCourseWare
* Computation in the Brain
* 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.
* Introduction to Neurons and Neuronal Networks, Neuroscience Online (electronic neuroscience textbook)
* 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
* 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)
* 延迟脉冲网络(波干扰网络)
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[[Category:Neural circuits|*]]
[[Category:Cognition]]
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Category:Cognition
* 类别: 认知
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