Learning and Memory in Neural Networks

Neural networks consist of computational units (neurons) that are linked by a directed graph with some degree of connectivity (network). The connections comprising the edges in the graph are termed weights. As the name suggests the magnitude of the weight determines the magnitude of the effect that the connecting neuron can have upon its target partner. In caricature, neural networks use the many parallel operations of simple units to perform computations with uncertain data, rather than serial operations with logical blocks to perform computations with exact data. Neural networks are useful computational devices for learning data classifications, for autoassociative (content addressable) memories and for associative (classical conditioning) memories. In this brief, neural networks performing each of these tasks are introduced, respectively: The multilayer perceptron, the Hopfield network and the associative network.