Editing Artificial neuron (section)

{{short description|Mathematical function conceived as a crude model}}
[[File:Artificial neuron structure.svg|alt=Artificial neuron structure|thumb|306x306px|Artificial neuron structure]]

An '''artificial neuron''' is a [[Function (mathematics)|mathematical function]] conceived as a [[Mathematical model|model]] of a [[neuron|biological neuron]] in a [[neural network]]. The artificial neuron is the elementary unit of an ''[[Neural network (machine learning)|artificial neural network]]''.<ref>{{Cite conference |author=Rami A. Alzahrani |author2=Alice C. Parker |title=Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling |book-title=Proceedings of International Conference on Neuromorphic Systems 2020 |location=New York |publisher=Association for Computing Machinery |language=en |article-number=19 |isbn=978-1-4503-8851-1 |doi=10.1145/3407197.3407204|s2cid=220794387|doi-access=free}}</ref>

The design of the artificial neuron was inspired by biological [[neural circuit]]ry. Its inputs are analogous to [[excitatory postsynaptic potential]]s and [[inhibitory postsynaptic potential]]s at neural [[dendrite]]s, or {{vanchor|activation}}. Its weights are analogous to [[synaptic weight]]s, and its output is analogous to a neuron's [[action potential]] which is transmitted along its [[axon]].

Usually, each input is separately [[weighting|weighted]], and the sum is often added to a term known as a ''bias'' (loosely corresponding to the [[threshold potential]]), before being passed through a [[Nonlinear system|nonlinear function]] known as an [[activation function]]. Depending on the task, these functions could have a [[Sigmoid function|sigmoid]] shape (e.g. for [[binary classification]]), but they may also take the form of other nonlinear functions, [[piecewise]] linear functions, or [[#Step function|step functions]]. They are also often [[Monotonic function|monotonically increasing]], [[Continuous function|continuous]], [[Differentiable function|differentiable]], and [[Bounded function|bounded]]. Non-monotonic, unbounded, and oscillating activation functions with multiple zeros that outperform sigmoidal and [[Rectifier (neural networks)|ReLU-like]] activation functions on many tasks have also been recently explored. The threshold function has inspired building [[logic gate]]s referred to as threshold logic; applicable to building [[logic circuit]]s resembling brain processing. For example, new devices such as [[memristor]]s have been extensively used to develop such logic.<ref>{{Cite journal|last1=Maan|first1=A. K.|last2=Jayadevi|first2=D. A.|last3=James|first3=A. P.|date=1 January 2016|title=A Survey of Memristive Threshold Logic Circuits|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=PP|issue=99|pages=1734–1746|doi=10.1109/TNNLS.2016.2547842|pmid=27164608|issn=2162-237X|arxiv=1604.07121|bibcode=2016arXiv160407121M|s2cid=1798273}}</ref>

The artificial neuron activation function should not be confused with a linear system's [[transfer function]].

An artificial neuron may be referred to as a '''semi-linear unit''', '''Nv neuron''', '''binary neuron''', '''linear threshold function''', or '''McCulloch–Pitts''' ('''MCP''') '''neuron''', depending on the structure used.

Simple artificial neurons, such as the McCulloch–Pitts model, are sometimes described as "caricature models", since they are intended to reflect one or more neurophysiological observations, but without regard to realism.<ref>
{{cite book |author=F. C. Hoppensteadt and E. M. Izhikevich |title=Weakly connected neural networks |publisher=Springer |year=1997 |isbn=978-0-387-94948-2 |page=4}}</ref> Artificial neurons can also refer to [[artificial cell]]s in [[#Physical artificial cells|neuromorphic engineering]] that are similar to natural physical neurons.