Editing Artificial neuron (section)

== McCulloch–Pitts (MCP) neuron ==
{{Main|Perceptron}}
An MCP neuron is a kind of restricted artificial neuron which operates in discrete time-steps. Each has zero or more inputs, and are written as <math>x_1, ..., x_n</math>. It has one output, written as <math>y</math>. Each input can be either ''excitatory'' or ''inhibitory''. The output can either be ''quiet'' or ''firing''. An MCP neuron also has a threshold <math>b \in \{0, 1, 2, ...\}</math>.

In an MCP neural network, all the neurons operate in synchronous discrete time-steps of <math>t = 0, 1, 2, 3, ...</math>. At time <math>t+1</math>, the output of the neuron is <math>y(t+1) = 1</math> if the number of firing excitatory inputs is at least equal to the threshold, and no inhibitory inputs are firing; <math>y(t+1)=0</math> otherwise.

Each output can be the input to an arbitrary number of neurons, including itself (i.e., self-loops are possible). However, an output cannot connect more than once with a single neuron. Self-loops do not cause contradictions, since the network operates in synchronous discrete time-steps.

As a simple example, consider a single neuron with threshold 0, and a single inhibitory self-loop. Its output would oscillate between 0 and 1 at every step, acting as a "clock".

Any [[Finite-state machine|finite state machine]] can be simulated by a MCP neural network.<ref name=":0">{{Cite book |last=Minsky |first=Marvin Lee |title=Computation: Finite and Infinite Machines |date=1967-01-01 |publisher=Prentice Hall |isbn=978-0-13-165563-8 |language=English}}</ref> Furnished with an infinite tape, MCP neural networks can simulate any [[Turing machine]].<ref>{{Cite journal |last1=McCulloch |first1=Warren S. |last2=Pitts |first2=Walter |date=1943-12-01 |title=A logical calculus of the ideas immanent in nervous activity |url=https://doi.org/10.1007/BF02478259 |journal=The Bulletin of Mathematical Biophysics |language=en |volume=5 |issue=4 |pages=115–133 |doi=10.1007/BF02478259 |issn=1522-9602|url-access=subscription }}</ref>