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

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

{{About||neural networks in computers|artificial neural network|projections from one region of the nervous system to another|neural pathway}}

{{About||neural networks in computers|artificial neural network|projections from one region of the nervous system to another|neural pathway}}

[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] ]]

[[File:Blausen 0657 MultipolarNeuron.png|thumb|300px|Anatomy of a [[multipolar neuron]] |链接=Special:FilePath/Blausen_0657_MultipolarNeuron.png]]

A '''neural circuit''' is a population of [[neuron]]s interconnected by [[synapse]]s to carry out a specific function when activated.<ref name="Neuro">{{cite book |last1=Purves |first1=Dale |title=Neuroscience |date=2011 |publisher=Sinauer |location=Sunderland, Mass. |isbn=9780878936953 |page=507 |edition= 5th}}</ref> Neural circuits interconnect to one another to form [[large scale brain networks]].<ref name="CEI">{{cite web |title=Neural Circuits {{!}} Centre of Excellence for Integrative Brain Function |url=https://www.brainfunction.edu.au/research/research-themes/neural-circuits/ |website=Centre of Excellence for Integrative Brain Function |access-date=4 June 2018 |language=en-AU |date=13 June 2016}}</ref> Biological [[neural network]]s have inspired the design of [[artificial neural network]]s, but artificial neural networks are usually not strict copies of their biological counterparts.

A '''neural circuit''' is a population of [[neuron]]s interconnected by [[synapse]]s to carry out a specific function when activated.<ref name="Neuro">{{cite book |last1=Purves |first1=Dale |title=Neuroscience |date=2011 |publisher=Sinauer |location=Sunderland, Mass. |isbn=9780878936953 |page=507 |edition= 5th}}</ref> Neural circuits interconnect to one another to form [[large scale brain networks]].<ref name="CEI">{{cite web |title=Neural Circuits {{!}} Centre of Excellence for Integrative Brain Function |url=https://www.brainfunction.edu.au/research/research-themes/neural-circuits/ |website=Centre of Excellence for Integrative Brain Function |access-date=4 June 2018 |language=en-AU |date=13 June 2016}}</ref> Biological [[neural network]]s have inspired the design of [[artificial neural network]]s, but artificial neural networks are usually not strict copies of their biological counterparts.

==Early study==

==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]].]]

[[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]].|链接=Special:FilePath/Cajal_actx_inter.jpg]]

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 [[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.

==Connections between neurons==

==Connections between neurons==

{{See also|Synapse}}

{{See also|Synapse}}

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

[[File:Leg Neural Network.jpg|thumb|Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013)|链接=Special:FilePath/Leg_Neural_Network.jpg]]

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.

==Circuitry==

==Circuitry==

[[File:Model of Cerebellar Perceptron.jpg|thumb|Model of a neural circuit in the [[cerebellum]]]]

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

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

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