UNSUPERVISED, SUPERVISED, AND REINFORCED LEARNING VIA SPIKING COMPUTATION

摘要

The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module.

主权项

1. A method for feature extraction, comprising:
providing input to a first neural module in a neural network comprising a plurality of neural modules interconnected via a plurality of weighted synaptic connections; extracting one or more input features from said input as said input propagates from said first neural module through said neural network via at least one of said weighted synaptic connections; providing output to a second neural module in said neural network; extracting one or more output features from said output as said output propagates from said second neural module through said neural network via at least one of said weighted synaptic connections; and associating said one or more input features with said one or more output features.