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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 transmission。1959年，神经科学家，沃伦·麦卡洛克Warren Sturgis和 沃尔特·皮茨Walter Pitts 发表了关于神经网络处理的第一部著作。<ref name=":1" /> 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中加成)。后来的模型也提供了兴奋性和抑制性突触传递。

= = 早期研究 = = 对神经网络的早期研究见于赫伯特 · 斯宾塞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==

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——突触后膜上的电位将在细胞体中进行加成。如果神经元在轴突丘处的去极化超过阈值，就会发生动作电位，动作电位沿着轴突向下传递到末端，将信号传递给其他神经元。兴奋性和抑制性突触传递主要通过兴奋性突触后电位(EPSPs)和抑制性突触后电位(IPSPs)实现。

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.

在电生理层面上，存在着改变个体突触(突触可塑性)和个体神经元(内禀可塑性)的反应特征的各种现象。这些可塑性通常分为短期可塑性和长期可塑性。长期突触可塑性通常被认为是最有可能的记忆底物。通常来说，“神经可塑性”指的是由活动或经历引起的大脑变化。

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]].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。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)或减少(抑制)。通过长期增强(LTP)或抑制(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增加现有受体的效能。

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⁺) 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⁺) 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.

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.