Autoassociative Network. A neural network (usually a multilayer perceptron) designed to reproduce its inputs at its outputs, while "squeezing" the data through a lower-dimensionality middle layer.
An autoassociative network is a network whose inputs and targets are the same. That is, the net must find a mapping from an input to itself.
You can use an auto-associative network to estimate a value. Simply train an auto-associative network on complete patterns then give the network an incomplete pattern and read off the missing value on the output units.
Spreading activation is a method for searching associative networks, neural networks, or semantic networks.
The Instar-Outstar structure belongs to associative networks and was originally invented by Karl Steinbuch in Germany. He wrote a book on it in 1963.
Hendrix, G. 1979. Encoding Knowledge in Partitioned Networks. In Associative Networks, ed. Findler, N., 51-92. New York: Academic Press.
 See also: Neural network, Percept, Mapping, Perceptron, Classification
  
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