“神经回路”的版本间的差异
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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. | 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" /> 1949年，赫布Hebb在其理论（即赫布理论Hebbian theory）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 | + | = = 早期研究 = = 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" /> 1949年，赫布Hebb在其理论（即赫布理论Hebbian theory）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neuroscientist, 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" /> 他们从理论上证明了人工神经元网络可以实现逻辑logical、算术arithmetic和符号功能symbolic。生物神经元的简化模型models of biological neurons由此建立起来，现在通常被称为感知器perceptrons或人工神经元artificial neurons。这些简单的模型解释了神经加成作用neural summation(即突触后膜上的电位将在细胞体cell body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== | ||
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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 [[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神经加成neural summation——突触后膜postsynaptic membrane上的电位potentials将在细胞体中进行加成。如果神经元在轴突丘axon hillock处的去极化depolarization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。 | |
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 [[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. | ||
| − | + | 在电生理electrophysiological层面上，存在着改变个体突触(突触可塑性synaptic plasticity)和个体神经元(内禀可塑性intrinsic plasticity)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memory底物。通常来说，“神经可塑性neuroplasticity”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]]. |
| + | |||
| + | 连接表现出时间和空间特征。时间特征是指突触传递的持续修饰的活动依赖的效能，称为峰时依赖的可塑性splke-timing-dependent plasticity。多项研究发现，根据突触前神经元的活动，这种传递的突触效能可以经历短期的增加(称为易化facilitation)或减少(抑制depression)。通过长期增强long-term potentiation(LTP)或抑制depression(LTD)诱导突触效能的长期变化，在很大程度上取决于兴奋性突触后电位excitatory postsynaptic potential和突触后动作电位的相对起病时间。LTP是由一系列动作电位引起的各种生化反应引起的。最终这些反应导致突触后神经元细胞膜上表达新的受体或通过磷酸化phosphorylation增加现有受体的效能。 | ||
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 [[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. | ||
| − | + | 反向传播的动作电位是不可能发生的，因为当动作电位沿着轴突的某一特定节段传递之后，电压门控钠通道voltage-gated sodium上的m门m gate关闭，从而阻止h门h gate的任何瞬态打开，以免引起细胞内钠离子浓度的变化。并阻止动作电位的产生回到细胞体内。然而，在某些细胞中，神经反向传播neural backpropagation确实通过树突分支dendritic branching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 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. | ||
2022年4月27日 (三) 08:59的版本
此词条由神经动力学读书会词条梳理志愿者（Glh20100487）翻译审校，未经专家审核，带来阅读不便，请见谅
关于计算机中使用的神经网络，请见人工神经网络artificial neural network。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neural pathway。
A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated.[1] Neural circuits interconnect to one another to form large scale brain networks.[2] 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neural circuit由一群神经元neuron构成，通过突触synapse相互连接，被激活时，就可以执行某种特定功能。[1] 神经回路彼此再连接，形成大尺度脑网络large scale brain networks。[2] 生物神经网络Biological neural network启发了人工神经网络artificial neural network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。
Early study
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).[3] 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.[4] 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)。[3] 1949年，赫布Hebb在其理论（即赫布理论Hebbian theory）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neuroscientist, 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。[4] 他们从理论上证明了人工神经元网络可以实现逻辑logical、算术arithmetic和符号功能symbolic。生物神经元的简化模型models of biological neurons由此建立起来，现在通常被称为感知器perceptrons或人工神经元artificial neurons。这些简单的模型解释了神经加成作用neural summation(即突触后膜上的电位将在细胞体cell body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
= = 神经元之间的连接 = =
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.
大脑神经元之间的连接比人工神经元artificial neuron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connectionist neural computing model。大脑神经元之间的基本连接是突触synapse,包括：化学突触chemical synapse和电突触electrical synapse。
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)
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)
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.[5]
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neurexin的突触前蛋白在这一过程中起着核心作用。[5]
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).
神经元工作的其中一个原理是神经加成neural summation——突触后膜postsynaptic membrane上的电位potentials将在细胞体中进行加成。如果神经元在轴突丘axon hillock处的去极化depolarization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。
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electrophysiological层面上，存在着改变个体突触(突触可塑性synaptic plasticity)和个体神经元(内禀可塑性intrinsic plasticity)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memory底物。通常来说，“神经可塑性neuroplasticity”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 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.
连接表现出时间和空间特征。时间特征是指突触传递的持续修饰的活动依赖的效能，称为峰时依赖的可塑性splke-timing-dependent plasticity。多项研究发现，根据突触前神经元的活动，这种传递的突触效能可以经历短期的增加(称为易化facilitation)或减少(抑制depression)。通过长期增强long-term potentiation(LTP)或抑制depression(LTD)诱导突触效能的长期变化，在很大程度上取决于兴奋性突触后电位excitatory postsynaptic potential和突触后动作电位的相对起病时间。LTP是由一系列动作电位引起的各种生化反应引起的。最终这些反应导致突触后神经元细胞膜上表达新的受体或通过磷酸化phosphorylation增加现有受体的效能。
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.
反向传播的动作电位是不可能发生的，因为当动作电位沿着轴突的某一特定节段传递之后，电压门控钠通道voltage-gated sodium上的m门m gate关闭，从而阻止h门h gate的任何瞬态打开，以免引起细胞内钠离子浓度的变化。并阻止动作电位的产生回到细胞体内。然而，在某些细胞中，神经反向传播neural backpropagation确实通过树突分支dendritic branching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 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.
大脑中的某一神经元需要一个单一的信号到神经肌肉连接neuromuscular junction，刺激突触后肌肉细胞的收缩。然而，在脊髓中，产生放电需要至少75个传入神经元afferent。由于神经元之间的时间常数变化，情形变得更加复杂，因为一些细胞会比其他细胞在更长的一段时间内感受到兴奋性突触后电位（EPSPs）。
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.
虽然在发育状态的脑developing brain突触中，突触抑制已经被广泛观察到，但能够推断的是，它在成人大脑中得到完善。
Circuitry
电路
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.[6]
神经回路的典型例子是海马体hippocampus中的三突触回路trisynaptic circuit。另一个是连接下丘脑hypothalamus和边缘叶limbic lobe的帕佩兹回路Papez circuit。在皮层-基底神经节-丘脑-皮层环路cortico-basal ganglia-thalamo-cortical loop中有几个神经回路。这些回路在皮层、基底神经节basal ganglia、丘脑之间传递信息，并将信息传回皮层。基底神经节内最大的结构，纹状体striatum，被认为有自己的内部微电路。[6]
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.[7]
脊髓spinal cord中的神经回路称为中央模式发生器central pattern generator，负责控制与节律性行为有关的运动指令。节律性行为包括行走、排尿urination和射精ejaculation。中枢模式发生器由不同组的脊髓中间神经元spinal interneuron组成。[7]
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.[8]
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、收敛电路、反射电路和并联后放电电路。[8]
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.[8]
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motor neuron的初始输入可以刺激到成千上万的肌纤维。[8]
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.[8]
在收敛电路中，来自多个源的输入收敛成一个输出，只影响一个神经元或神经元池。脑干brainstem的呼吸中枢respiratory center就是这种回路的典型例子，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。[8]
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.[8]
反射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。这种类型的反射电路在呼吸中枢被发现，它向呼吸肌肉respiratory muscles发送信号引起吸入。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epileptic seizure发作中起作用。[8]
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.[8]
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reflex的反射弧reflex arc中。[8]
Study methods
研究方法
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neuroimaging。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，要么是作为一种用高分辨率图片更好地评估大脑损伤的方法，要么是通过检查不同大脑区域的相对激活情况。这些技术可能包括功能性磁共振成像functional magnetic resonance imaging(fMRI)、脑正电子发射断层扫描brain positron emission tomography(brain PET)和计算机轴向断层扫描computed axial tomography(CAT)。功能性神经成像技术Functional neuroimaging使用特定的大脑成像技术对大脑进行扫描，通常是当一个人在做一项特定的任务时，试图了解大脑特定区域的激活与任务之间的关系。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hemodynamic activity(使用 BOLD 对比成像) ，这与神经活动、PET密切相关，同时也会使用脑电图electroencephalography(EEG)。
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.连接主义者Connectionist模型是对不同的表征假设、信息处理假设和信号传输假设的测试平台。对这类模型的损伤研究，例如人工神经网络artificial neural networks故意破坏部分节点，以了解网络的运行情况，也可以对几个细胞组装的工作产生重要的见解。同样，模拟神经系统条件下功能失调的神经递质(例如，帕金森氏症Parkinson's患者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。从这些模型中得到的预测可以在患者中进行测试，或者通过药物操作进行测试，这些研究反过来可以用来模型反馈，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."[9] 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.[10] 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.[11]
在神经生物学neurobiology中，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实现。1972年，巴洛宣布了“单一神经元革命”：“我们的感知是由从大量沉默细胞中选择的少量神经元的活动引起的。‘’[9] 这种方法是受到两年前提出的祖母细胞grandmother的启发。巴洛提出了神经元学说的“五大信条”。最近对“祖母细胞grandmother cell”和稀疏编码现象的研究进一步完善了这些观点。[10]单细胞实验使用位于内侧颞叶(海马和周围皮层)的颅内电极。度量集中concentration of measure理论(随机分离定理)的现代发展及其在人工神经网络artificial neural networks中的应用为高维大脑中小型神经系统集成的意想不到的有效性提供了数学背景。[11]
Clinical significance
Sometimes neural circuitries can become pathological and cause problems such as in Parkinson's disease when the basal ganglia are involved.[12] Problems in the Papez circuit can also give rise to a number of neurodegenerative disorders including Parkinson's.
临床意义
在某些情况下，当基底神经节basal ganglia受累时，神经环路会变成病理性的，继而引起问题，如帕金森病Parkinson's disease。[12] 帕佩兹回路Papez circuit发生问题也会导致包括帕金森氏症在内的一系列神经退行性疾病neurodegenerative disorders。
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
- ↑ 1.0 1.1 Purves, Dale (2011). Neuroscience (5th ed.). Sunderland, Mass.: Sinauer. p. 507. ISBN 9780878936953.
- ↑ 2.0 2.1 "Neural Circuits | Centre of Excellence for Integrative Brain Function". Centre of Excellence for Integrative Brain Function (in English). 13 June 2016. Retrieved 4 June 2018.
- ↑ 3.0 3.1 Michael S. C. Thomas; James L. McClelland. "Connectionist models of cognition" (PDF). Stanford University. Archived from the original (PDF) on 2015-09-06. Retrieved 2015-08-31.
- ↑ 4.0 4.1 J. Y. Lettvin; H. R. Maturana; W. S. McCulloch; W. H. Pitts (1959), "What the frog's eye tells the frog's brain.", Proc. Inst. Radio Engr., no. 47, pp. 1940–1951
- ↑ 5.0 5.1 Südhof, TC (2 November 2017). "Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits". Cell. 171 (4): 745–769. doi:10.1016/j.cell.2017.10.024. PMC 5694349. PMID 29100073.
- ↑ 6.0 6.1 Stocco, Andrea; Lebiere, Christian; Anderson, John R. (2010). "Conditional Routing of Information to the Cortex: A Model of the Basal Ganglia's Role in Cognitive Coordination". Psychological Review. 117 (2): 541–74. doi:10.1037/a0019077. PMC 3064519. PMID 20438237.
- ↑ 7.0 7.1 Guertin, PA (2012). "Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations". Frontiers in Neurology. 3: 183. doi:10.3389/fneur.2012.00183. PMC 3567435. PMID 23403923.
- ↑ 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 Saladin, K. Human anatomy (3rd ed.). McGraw-Hill. p. 364. ISBN 9780071222075.
- ↑ 9.0 9.1 Barlow, HB (December 1, 1972). "Single units and sensation: a neuron doctrine for perceptual psychology?". Perception. 1 (4): 371–394. doi:10.1068/p010371. PMID 4377168.
- ↑ 10.0 10.1 Quian Quiroga, R; Reddy, L; Kreiman, G; Koch, C; Fried, I (Jun 23, 2005). "Invariant visual representation by single neurons in the human brain". Nature. 435 (7045): 1102–1107. doi:10.1038/nature03687. PMID 15973409.
- ↑ 11.0 11.1 Gorban, Alexander N.; Makarov, Valeri A.; Tyukin, Ivan Y. (July 2019). "The unreasonable effectiveness of small neural ensembles in high-dimensional brain". Physics of Life Reviews. 29: 55–88. arXiv:1809.07656. doi:10.1016/j.plrev.2018.09.005. PMID 30366739.
- ↑ 12.0 12.1 French, IT; Muthusamy, KA (2018). "A Review of the Pedunculopontine Nucleus in Parkinson's Disease". Frontiers in Aging Neuroscience. 10: 99. doi:10.3389/fnagi.2018.00099. PMC 5933166. PMID 29755338.
Further reading
- 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
- 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)
- 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)
Category:Cognition
- 类别: 认知
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