| Abstract: | Modelers have come up with many different learning rules for neural networks. When a teacher specifies the correct output, error-driven rules work better than pure Hebb rules in which the changes in synapse strength depend on the correlation between pre and postsynaptic activities. But for unsupervised learning, Hebb rules can be very effective if they are combined with suitable normalization or "unlearning" terms to prevent the synapses growing without bound. Hebb rules that use rates of change of activity instead of activity itself are useful for discovering perceptual invariants and may also provide a way of implementing error-driven learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved) |
