258 13 Local, Global and Glocal Knowledge Representation 13.5 Neural Foundations of Learning Now we move from knowledge representation to learning — which is after all nothing but the adaptation of represented knowledge based on stimulus, reinforcement and spontaneous activity. While our focus in this chapter is on representation, it’s not possible for us to make our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of Hebbian learning. The original Hebbian learning rule, proposed by Donald Hebb in his 1949 book [Heb49], was roughly 1. The weight of the synapse 2 > y increases if x and y fire at roughly the same time 2. The weight of the synapse « — y decreases if x fires at a certain time but y does not Over the years since Hebb’s original proposal, many neurobiologists have sought evidence that the brain actually uses such a method. One of the things they have found, so far, is a lot of evidence for the following learning rule [DC02, LS05]: 1. The weight of the synapse x > y increases if x fires shortly before y does 2. The weight of the synapse « — y decreases if x fires shortly after y does The new thing here, not foreseen by Donald Hebb, is the “postsynaptic depression” involved in rule component 2. Now, the simple rule stated above does not sum up all the research recently done on Hebbian- type learning mechanisms in the brain. The real biological story underlying these approximate rules is quite complex, involving many particulars to do with various neurotransmitters. Ill understood details aside, however, there is an increasing body of evidence that not only does this sort of learning occur in the brain, but it leads to distributed experience-based neural modification: that is, one instance synaptic modi