Editing Artificial neuron (section)

==Biological models==
{{main|Biological neuron model}}
[[File:Neuron3.svg|thumb|right|400px|Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals]]

Artificial neurons are designed to mimic aspects of their biological counterparts. However a significant performance gap exists between biological and artificial neural networks. In particular single biological neurons in the human brain with oscillating activation function capable of learning the [[Exclusive or|XOR function]] have been discovered.<ref>{{Cite journal|last1=Gidon|first1=Albert|last2=Zolnik|first2=Timothy Adam|last3=Fidzinski|first3=Pawel|last4=Bolduan|first4=Felix|last5=Papoutsi|first5=Athanasia|last6=Poirazi|first6=Panayiota|author-link6=Panayiota Poirazi| last7=Holtkamp|first7=Martin|last8=Vida|first8=Imre|last9=Larkum|first9=Matthew Evan|date=2020-01-03|title=Dendritic action potentials and computation in human layer 2/3 cortical neurons|journal=Science|volume=367|issue=6473|pages=83–87|doi=10.1126/science.aax6239|pmid=31896716|bibcode=2020Sci...367...83G|s2cid=209676937|doi-access=free}}</ref>
 
* [[Dendrites]] – in biological neurons, dendrites act as the input vector. These dendrites allow the cell to receive signals from a large (>1000) number of neighboring neurons. As in the above mathematical treatment, each dendrite is able to perform "multiplication" by that dendrite's "weight value." The multiplication is accomplished by increasing or decreasing the ratio of synaptic neurotransmitters to signal chemicals introduced into the dendrite in response to the synaptic neurotransmitter. A negative multiplication effect can be achieved by transmitting signal inhibitors (i.e. oppositely charged ions) along the dendrite in response to the reception of synaptic neurotransmitters.
* [[Soma (biology)|Soma]] – in biological neurons, the soma acts as the summation function, seen in the above mathematical description. As positive and negative signals (exciting and inhibiting, respectively) arrive in the soma from the dendrites, the positive and negative ions are effectively added in summation, by simple virtue of being mixed together in the solution inside the cell's body.
* [[Axon]] – the axon gets its signal from the summation behavior which occurs inside the soma. The opening to the axon essentially samples the electrical potential of the solution inside the soma. Once the soma reaches a certain potential, the axon will transmit an all-in signal pulse down its length. In this regard, the axon behaves as the ability for us to connect our artificial neuron to other artificial neurons.

Unlike most artificial neurons, however, biological neurons fire in discrete pulses. Each time the electrical potential inside the soma reaches a certain threshold, a pulse is transmitted down the axon. This pulsing can be translated into continuous values. The rate (activations per second, etc.) at which an axon fires converts directly into the rate at which neighboring cells get signal ions introduced into them. The faster a biological neuron fires, the faster nearby neurons accumulate electrical potential (or lose electrical potential, depending on the "weighting" of the dendrite that connects to the neuron that fired). It is this conversion that allows computer scientists and mathematicians to simulate biological neural networks using artificial neurons which can output distinct values (often from −1 to 1).

===Encoding===
Research has shown that [[unary coding]] is used in the neural circuits responsible for [[birdsong]] production.<ref>{{cite book|editor1-last=Squire|editor1-first=L.|editor2-last=Albright|editor2-first=T.|editor3-last=Bloom|editor3-first=F.|editor4-last=Gage|editor4-first=F.|editor5-last=Spitzer|editor5-first=N.|title=Neural network models of birdsong production, learning, and coding|date=October 2007|publisher=Elservier|location=New Encyclopedia of Neuroscience|url=https://clm.utexas.edu/fietelab/Papers/birdsong_review_topost.pdf|access-date=12 April 2015|archive-url=https://web.archive.org/web/20150412190625/https://clm.utexas.edu/fietelab/Papers/birdsong_review_topost.pdf|archive-date=2015-04-12}}</ref><ref>{{cite journal | last1 = Moore | first1 = J.M. | display-authors = etal | year = 2011| title = Motor pathway convergence predicts syllable repertoire size in oscine birds | journal = Proc. Natl. Acad. Sci. USA | volume = 108 | issue = 39| pages = 16440–16445 | doi = 10.1073/pnas.1102077108 | pmid = 21918109 | pmc = 3182746 | bibcode = 2011PNAS..10816440M | doi-access = free }}</ref> The use of unary in biological networks is presumably due to the inherent simplicity of the coding. Another contributing factor could be that unary coding provides a certain degree of error correction.<ref>{{cite arXiv|eprint=1411.7406|title=Error Correction Capacity of Unary Coding|first=Pushpa Sree|last=Potluri|date=26 November 2014|class=cs.IT}}</ref>