Mcculloch pitts neuron model:

This is simplified model of real neurons, known as Threshold Logic Unit.

A set of synapsesc (i.e connections) brings the activations from the other neurons.

A processing unit sums the inputs, the applies the non-linear activation funcation (i.e threshold / transfer function).

A output line transmit the result to other neurons.

In other word, the input to a neuron arrives in the form of signals.The signals build up in the cell. Finally the cells fires(discharges) through the output. The cell can start building up signals again.

Functions:

The function y=f(X) describes a relationship , an input-ouput mapping rom x to y. threshold or sign function sgn(x) : define as

threshold or sign sigmod : define as a smooth (differentiable) form of threshold function

Mcculloch pitts neuron Equation:

The figure above show the simplified model of a real neuron , as a threshold logical unit .

The equation of output of mcculloch pitts neuron as a function of 1 to n input is given as

        sum=

The mcculloch pitts neuron model is extremely simplifield model of real biological neurons. some of its missing features includes: non binary inputs-outputs, non linear summation, smooth thresholding , stochastic(non-deterministic) snd tempory information processing.

• It inspred by Biological Neural network.

• It allows only Binary Value(0,1).

• It has threshold function as activation function.

• It is first mathmatical model of Biological Neuron