Wikipedia says An Artificial Neuron or The Mcculloch Pitts Neuron is a mathematical function conceived as a model of biological neurons, a neural network.
And now we are going to see What is Mcculloch Pitts Neuron and we are going to learn the Mathematics behind this neuron in the simplest way.
So, Let’s Get Started.
First of all, What is Biological Neuron?
A biological neuron consists Fours Parts Mainly,

Dendrite – Receives signals from other neurons.

Synapse – Point of connection to other neurons.

Soma – It’s the CPU, it processes the information.

Axon – Transmits the output of the neuron.
Mccullouch Pitts Neuron
As you can see {X_{1, }X_{2, }X_{3, }…, X_{n}} ∈ {0,1} are the Inputs and Y ∈ {0,1} is the Output. And f and g are the Functions.
f is the Activation Function and g is the PreActivation Function.
There are two types of inputs, One is Excitatory Input, which is dependent and another is Inhibitory Input, which is independent input.
Here {X_{1, }X_{2, }X_{3, }…, X_{n}} ∈ {0,1} are the Excitatory Inputs.
Now, let’s see the Math behind this neuron.
The output will be 1 if g(x) is Greater than equal to ( ≥ ) the threshold parameter and the output will be 0 if g(x) is less than ( < ) the threshold parameter.
I hope its clear now.
Notes: –
A single Mcculloch Pitts neuron can be used to represent Boolean functions ( AND, OR, NOR etc. ) which are linearly separable.
Linear Separability: There exists a line ( plane ) such that all inputs which produce a 1 lie on one side of the line ( plane ) and all inputs which produce a 0 lie on another side of the line ( plane ).
Source: NPTEL’s Deep Learning Course