Supervised training and pattern matching techniques for neural networks

TL;DR: In this paper, the authors describe a supervised learning and cascaded training of a neural network, where a first spike at a classifier neuron from a processing neuron in response to training data, and receiving an out-of-band communication of a second desired (artificial) spike at the classifier neurons that corresponds to the classification of the training data are strengthened.

Abstract: Systems and methods for supervised learning and cascaded training of a neural network are described. In an example, a supervised process is used for strengthening connections to classifier neurons, with a supervised learning process of receiving a first spike at a classifier neuron from a processing neuron in response to training data, and receiving an out-of-band communication of a second desired (artificial) spike at the classifier neuron that corresponds to the classification of the training data. As a result of spike timing dependent plasticity, connections to the classifier neuron are strengthened. In another example, a cascaded technique is disclosed to generate a plurality of trained neural networks that are separately initialized and trained based on different types or forms of training data, which may be used with cascaded or parallel operation of the plurality of trained neural networks.