WK7 – Hebbian Learning. CS 476: Networks of Neural Computation WK7 – Hebbian Learning Dr. Stathis Kasderidis Dept. of Computer Science University of Crete Spring Semester, 2009. Contents. Introduction to Hebbian Learning Definitions on Pattern Association Pattern Association Network
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WK7 – Hebbian Learning
CS 476: Networks of Neural Computation
WK7 – Hebbian Learning
Dr. Stathis Kasderidis
Dept. of Computer Science
University of Crete
Spring Semester, 2009
Contents
Contents
Hebbian Learning
Hebb. Learn.
Hebbian Learning-1
Hebb. Learn.
Hebbian Learning-2
Hebb. Learn.
Hebbian Learning-3
Hebb. Learn.
Hebbian Learning-4
Hebb. Learn.
Hebbian Learning-5
wkj (n)=F(yk(n), xj(n))
where F(•,•) is a function of both signals. The above formula can take many specific forms. Typical examples are:
Hebb. Learn.
Hebbian Learning-6
wkj (n)=yk(n) xj(n)
where is a learning rate. This form emphasises the correlational nature of a Hebbian synapse. However this simple rule leads to an exponential growth of the weights (becomes unbounded). Thus we need to mechanism to stop the unbounded increase of the weights. One such is the following.
Hebb. Learn.
Hebbian Learning-7
time-averaged value then the covariance form is defined by:
wkj (n)=(yk(n)-y*) (xj(n)-x*)
Hebb. Learn.
Pattern Association
Patt. Assoc.
Pattern Association-1
Patt. Assoc.
Pattern Association-2
Patt. Assoc.
Pattern Association-3
Patt. Assoc.
Pattern Association Network
Associator
Pattern Association Network-1
Associator
Pattern Association Network-2
Associator
Pattern Association Network-3
Associator
Pattern Association Network-4
Associator
Pattern Association Network-5
Associator
Pattern Association Network-6
Associator
Pattern Association Network-7
Associator
Pattern Association Network-8
Associator
Correlations
Correlations
Correlations-1
Correlations
Correlations-2
Correlations
Correlations-3
Correlations
Correlations-4
Correlations
Correlations-5
Correlations
Correlations-6
Correlations
Examples
Examples
Examples-1
Examples
Examples-2
Examples
Examples-3
Examples
Examples-4
Examples
Examples-5
Examples
Examples-6
Examples
Examples-7
Examples
Examples-8
wkj (n)=ak(n) mj(n)
mi(n)=(1-)ai(n)+ mi(n-1)
Where is a constant which determines the contribution of memory and of current activation. ai(n)is the activation of the neuron at time n and is calculated in the usual way.
Examples
Examples-9
Examples
Conclusions
Conclusions