Neural network and method of neural network training

A method of operating a neural network, comprising:

• receiving training images via a plurality of inputs to the neural network, wherein the training images are one of received as a training input value array and codified as the training input value array during training of the neural network;
• organizing corrective weights of a plurality of synapses in a corrective weight array, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, and wherein each corrective weight is defined by a weight value;
• generating a neuron sum array via a plurality of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights corresponding to each synapse connected to the respective neuron;
• receiving, via a controller, desired images organized as a desired output value array;
• determining, via the controller, a deviation of the neuron sum array from the desired output value array and generate a deviation array;
• and
• modifying, via the controller, the corrective weight array using the determined deviation array, such that adding up the modified corrective weight values to determine the neuron sum array reduces the deviation of the neuron sum array from the desired output value array to generate a trained corrective weight array and thereby facilitate concurrent training of the neural network.

A non-transitory computer-readable storage device for operating an artificial neural network, the storage device encoded with instructions executable to:

• receive training images via a plurality of inputs to the neural network, wherein the training images are one of received as a training input value array and codified as the training input value array during training of the neural network;
• organize corrective weights of a plurality of synapses in a corrective weight array, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, and wherein each corrective weight is defined by a weight value;
• generate a neuron sum array via a plurality of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights corresponding to each synapse connected to the respective neuron;
• receive desired images organized as a desired output value array;
• determine a deviation of the neuron sum array from the desired output value array and generate a deviation array;
• and
• modify the corrective weight array using the determined deviation array, such that adding up the modified corrective weight values to determine the neuron sum array reduces the deviation of the neuron sum array from the desired output value array to generate a trained corrective weight array and thereby facilitate concurrent training of the neural network.

An apparatus for operating an artificial neural network, comprising:

• a means for receiving training images via a plurality of inputs to the neural network, wherein the training images are one of received as a training input value array and codified as the training input value array during training of the neural network;
• a means for organizing corrective weights of a plurality of synapses in a corrective weight array, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, and wherein each corrective weight is defined by a weight value;
• a means for generating a neuron sum array via a plurality of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights corresponding to each synapse connected to the respective neuron;
• a means for receiving desired images organized as a desired output value array;
• a means for determining a deviation of the neuron sum array from the desired output value array and generate a deviation array;
• and
• a means for modifying the corrective weight array using the determined deviation array, such that adding up the modified corrective weight values to determine the neuron sum array reduces the deviation of the neuron sum array from the desired output value array to generate a trained corrective weight array and thereby facilitate concurrent training of the neural network.