Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)
The complexity of learning in shallow I-Dimensional neural networks has been shown elsewhere to be linear in the size of the network. However, when the network has a huge number of units (as cortex has) even linear time might be unacceptable. Furthermore, the algorithm that was given to achieve this time was based on a single serial processor and was biologically implausible. In this work we consider the more natural parallel model of processing and demonstrate an expected-time complexity that is constant (i.e. dependent of the size of the network). This holds even when inter-node communication channels are short and local, thus adhering to more bio(cid:173) logical and VLSI constraints.