Chapter 2. Prehension

390 Appendices
between two units in proportion to the product of their simultaneous
activation. More formally, the Hebbian learning rule can be stated as
follows (see left side of Figure C.5):
Awij = Oi Oj (5)
where the output of neuron j is multiplied by the output of neuron i
and by a constant of proportionality q (the learning rate) to produce the
amount by which to change the weight between them. Resynaptic
signals and postsynaptic signals are used to modify the weights,
without benefit of a teacher (i.e., this is unsupervised learning). The
larger q is, the larger the changes in the weights. The advantage of
this rule is that information is locally available for determining how to
change the weights. A disadvantage of such a simple learning rule can
be seen in a network that is trying to associate one pattern with
another. Two patterns are presented and weights are modified so that,
if only one pattern is presented the network can produce the second
one correctly. Using Hebbian learning, this can happen only when all
patterns are completely uncorrelated.
Hebbian learning rule Delta learning rule
Figure C.5 Two different learning rules for updating the synaptic
weights. On the left, Hebbian learning is shown, where weights
are adjusted in proportion to the product of two neurons’s
simultaneous activation. On the right, the delta rule is shown,
where a teacher is needed to compute the error between the desired
output and actual output.