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Storage of information in a network is thus localized in the connections and not in the nodes. This forms a significant departure from the symbolic language because there different functions are obtained by changing the algorithm of some, mostly central, processor. If a node from a network is identified with a processor, the elementary processes themselves are not modified, but changes in behavior arise as a consequence of linking up different nodes. The number of processors also differs widely in the symbolic and connectionist language.

A way of relating weight changes to the surrounding activations seems better suited for solving this problem. This is, in fact, a defining characteristic of competitive learning which will be discussed below. , 1985), which also uses a kind of Hebbian learning. This model possesses (stochastic) activations even when there is no stimulation present. It can thus function in two different states: without stimuli and with stimuli. The weight change is then simply made proportional to the difference between two probabilities.

2+1=3) are learned by a three layer network with back-propagation. First, all additions with one are learned and then the additions with two, while no more learning opportunities are 42 A connectionist approach to learning given with the ones. Testing of both series of additions shows catastrophic interference on the additions with one. Here the network should learn that, for instance, conditional on the presence of a I, a 3 should map onto a 4, whereas it should map onto a 5 when a 2 is present.

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AGS Experiments [1994-1996]


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