초록 ▼

A cortronic neural network defines connections between neurons in a number of regions using target lists, which identify the output connections of each neuron and the connection strength. Neurons are preferably sparsely interconnected between regions. Training of connection weights employs a three s

A cortronic neural network defines connections between neurons in a number of regions using target lists, which identify the output connections of each neuron and the connection strength. Neurons are preferably sparsely interconnected between regions. Training of connection weights employs a three stage process, which involves computation of the contribution to the input intensity of each neuron by every currently active neuron, a competition process that determines the next set of active neurons based on their current input intensity, and a weight adjustment process that updates and normalizes the connection weights based on which neurons won the competition process, and their connectivity with other winning neurons.

대표청구항 ▼

1. A distributed artificial neural network system, comprising:a plurality of distributed computers, each distributed computer storing in memory, at least one region of an artificial neural network, each region containing a plurality of the neurons, each neuron associated with a list of target neuron

1. A distributed artificial neural network system, comprising:a plurality of distributed computers, each distributed computer storing in memory, at least one region of an artificial neural network, each region containing a plurality of the neurons, each neuron associated with a list of target neurons to which the neuron has an output connection, the output connection having a connection weight, and wherein for at least one region on a distributed computer, the neurons in the region being connected to substantially less than all of the neurons in another region or the same region; an intensity calculation process, executed by the distributed computer, that determines an intensity of inputs to each neuron from the connection weights of the output connections of other neurons for which the neuron is a target neuron; a competition calculation process, executed by the distributed computer, that determines a subset of the neurons that are active neurons as a function of an intensity of inputs at each neuron; and a renormalization calculation process, executed by the distributed computer, that normalizes the connection weights of the output connections of a neuron to its target neurons by a scaling factor derived from the sum of the connection weights of the output connections, and that normalizes the input intensity of each neuron by a scaling factor derived from the sum of input connection weights of connections from other neurons.