  This paper presents a review of instantaneously trained neural networks
(ITNNs). These networks trade learning time for size and, in the basic model, a
new hidden node is created for each training sample. Various versions of the
corner-classification family of ITNNs, which have found applications in
artificial intelligence (AI), are described. Implementation issues are also
considered.
