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 comprising:
producing spiking computation in a neural network comprising a plurality of neural modules interconnected via weighted synaptic connections in an interconnection network, wherein each neural module comprises multiple digital neurons such that every neuron in a first neural module is connected to a corresponding neuron in a second neural module via a weighted synaptic connection; wherein said spiking computation comprises generating signals which define a set of time steps for operation of the neurons, and at each time step, each neuron based on its operational state determines whether to generate a firing event in response to firing events received as input signals from neurons in other neural modules, wherein each said input signal is weighted by the weighted synaptic connection communicating said input signal to said neuron.