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Artificial Neurons and its activation function - Tutorial Part3

Artificial Neurons and its activation function
Just like bio-logical neuron cell, the artificial neuron contains several inputs, output terminals and a computation unit (nucleus) at the center, as shown in the below figure. Each input connected to the neuron cell has its own weight represents the strength of the input signal.

Artificial Neuron


X1, X2, X3, …. Xn are the inputs of artificial neuron and these are multiplied with corresponding weights (W1, W2, W3, … Wn) before entering in to the nucleus. The nucleus will sum up all these weighted input values which produce “activation”.

The activation is given as       

Where Wj is the weight associated with the jth input (Xj).

McCulloch and Pitts were proposed a binary threshold unit as a computational model for an artificial neuron cell.
i.e., if the sum of the weighted inputs of the neuron cell is greater than threshold value gives a output ‘1’ otherwise is gives ‘0’ as output.

McCulloch-Pitts model of a artificial neuron cell






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