TRELIS: an optimal unsupervised local training rule

A new local non-Hebbian unsupervised training rule for limited interconnect structures (TRELIS) is derived. The training rule is based on the model of a neuron as a coherent activity detector. The highly parallel and local nature of the training rule allows efficient silicon VLSI implementation. Because of the locality of the training rule and its dependence solely on the excitation source, a network of any desired size is theoretically possible. The statistical correlation of the inputs with a computing node can be used as a feature or a symbol. Another property of this feature is that scaling of the two inputs by the same constant will not change their correlation coefficient or the node output. Simulation of a network of linear computing nodes with TRELIS for the detection of edges in a dynamically blurred image is included