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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