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The connections between neurons in the brain are much more complex than those of the [[artificial neuron]]s used in the [[connectionism|connectionist]] neural computing models of [[artificial neural network]]s. The basic kinds of connections between neurons are [[synapse]]s: both [[chemical synapse|chemical]] and [[electrical synapse]]s.

The connections between neurons in the brain are much more complex than those of the [[artificial neuron]]s used in the [[connectionism|connectionist]] neural computing models of [[artificial neural network]]s. The basic kinds of connections between neurons are [[synapse]]s: both [[chemical synapse|chemical]] and [[electrical synapse]]s.

大脑神经元之间的连接比人工神经元之间的连接要复杂得多，人工神经元常用于人工神经网络中的连接计算模型。大脑神经元之间的基本连接是突触,包括：化学突触和电突触。

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)

Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013)

The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called [[neurexin]]s are central to this process.<ref name="Sudhof">{{cite journal |last1=Südhof |first1=TC |title=Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits. |journal=Cell |date=2 November 2017 |volume=171 |issue=4 |pages=745–769 |doi=10.1016/j.cell.2017.10.024 |pmid=29100073|pmc=5694349 }}</ref>

The establishment of synapses enables the connection of neurons into millions of overlapping, and interlinking neural circuits. Presynaptic proteins called [[neurexin]]s are central to this process.<ref name="Sudhof">{{cite journal |last1=Südhof |first1=TC |title=Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits. |journal=Cell |date=2 November 2017 |volume=171 |issue=4 |pages=745–769 |doi=10.1016/j.cell.2017.10.024 |pmid=29100073|pmc=5694349 }}</ref>

突触的建立使得神经元能够连接成千上万个重叠的、相互连接的神经回路。被称为神经蛋白的突触前蛋白在这一过程中起着核心作用。<ref name="Sudhof" />

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。<ref name="Sudhof" />

One principle by which neurons work is [[Summation (neurophysiology)|neural summation]] – [[postsynaptic potential|potentials]] at the [[Chemical synapse|postsynaptic membrane]] will sum up in the cell body. If the [[depolarization]] of the neuron at the [[axon hillock]] goes above threshold an action potential will occur that travels down the [[axon]] to the terminal endings to transmit a signal to other neurons. Excitatory and inhibitory synaptic transmission is realized mostly by [[excitatory postsynaptic potentials]] (EPSPs), and [[inhibitory postsynaptic potentials]] (IPSPs).

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).

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.

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 [[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.

==Circuitry==

==Circuitry==

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

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

神经回路的典型例子是海马体中的三突触回路。另一个是连接下丘脑和边缘叶的帕佩兹回路。在皮层-基底神经节-丘脑-皮层环路中有几个神经回路。这些回路在皮层、基底神经节、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，被认为有自己的内部微电路。<ref name="Stocco" />

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。<ref name="Stocco" />

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 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>

脊髓中的神经回路称为中央模式发生器，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、排尿和射精。中枢模式发生器由不同组的脊髓中间神经元组成。<ref name="Guertin" />

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。<ref name="Guertin" />

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'''.<ref name="Saladin">{{cite book |last1=Saladin |first1=K |title=Human anatomy |publisher=McGraw-Hill |isbn=9780071222075 |page=364 |edition=3rd}}</ref>

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、混响电路和并联后放电电路。<ref name="Saladin" />

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。<ref name="Saladin" />

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

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

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

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

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。<ref name="Saladin" />

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。<ref name="Saladin" />

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

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

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。脑干的呼吸中枢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。<ref name="Saladin" />

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。<ref name="Saladin" />

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

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

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

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

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

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。<ref name="Saladin" />

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

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

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的反射弧中。<ref name="Saladin" />

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。<ref name="Saladin" />

==Study methods==

==Study methods==

{{See also|Neuropsychology|Cognitive neuropsychology}}

{{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 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.

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(fMRI)、脑正电子发射断层扫描(脑PET)和计算机轴向断层扫描(CAT)。功能性神经成像技术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(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(EEG)。

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)。

[[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.连接主义者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。

[[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.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.

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 name=":2">{{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>

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 name=":2">{{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>

在神经生物学中，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。‘’<ref name="Barlow1972" /> 这种方法是受到两年前提出的祖母细胞的启发。巴洛提出了神经元学说的“五大信条”。最近对“祖母细胞”和稀疏编码现象的研究进一步完善了这些观点。<ref name="QuianQuiroga2005" />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集成的意想不到的有效性提供了数学背景。<ref name=":2" />

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。‘’<ref name="Barlow1972" /> 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。<ref name="QuianQuiroga2005" />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。<ref name=":2" />

==Clinical significance==

==Clinical significance==

临床意义

临床意义

==See also==

==See also==