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 sensory 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 sensory input as said sensory input propagates from said first neural module through said neural network in a first direction via at least one of said plurality of weighted synaptic connections; providing motor output to a second neural module in said neural network; extracting one or more output features from said motor output as said motor output propagates from said second neural module through said neural network in a second direction via at least one of said plurality of weighted synaptic connections, wherein said first direction is opposite of said second direction; and associating said one or more input features with said one or more output features.