application of A.I. lecture_4

Feedforward unsupervised learning
“When an axon of a cell A is near enough to exicite a cell B and repeatedly and persistently takes place in firing it, some growth process or change takes place in one or both cells increasing the efficiency”
If oixj is positive the results is increase in weight else vice versa
For the same inputs for bipolar continuous activation function the final updated weight is given by

Can be explained for a layer of neurons
Example of competitive learning and used for unsupervised network training
Learning is based on the premise that one of the neurons in the layer has a maximum response due to the input x
This neuron is declared the winner with a weight
Separation of the input space into regions is based on whether the network response is positive or negative
Line of separation is called linear-separable line.
Example:-
AND function & OR function are linear separable Example
EXOR function Linearly inseparable. Example

Hebb learning rule is the simpliest one
The learning in the brain is performed by the change in the synaptic gap
When an axon of cell A is near enough to excite cell B and repeatedly keep firing it, some growth process takes place in one or both cells
According to Hebb rule, weight vector is found to increase proportionately to the product of the input and learning signal.