Patent ID: 8423490

Claim:
A neural network for classification of input information to particular classes comprising a first layer of neurons representing characteristic values for the input information and a second layer of neurons representing categories for the input information, the layers crosslinked with synaptic connections comprising forwardly-directed and backwardly-directed synaptic connections having assigned weights, wherein the neural network is trained using a reward based learning method that modifies the weights of the synaptic connections between neurons based on a categorization determination and an activity state determination of a neuron, such that: a) when a correct categorization is determined, 1) for each forwardly-directed and backwardly-directed synaptic connection between neurons of the first layer and neurons of the second layer, wherein the activity state of the neurons for that synaptic connection have been determined both to be active, the weight of the synaptic connection is strengthened, and 2) for each forwardly-directed synaptic connection between neurons of the first layer and neurons of the second layer, wherein the activity state of the first neuron has been determined to be active and the activity state of the second neuron has been determined to be inactive for that synaptic connection, the weight of the synaptic connection is weakened, and b) when an incorrect categorization is determined, 1) for each forwardly-directed and backwardly-directed synaptic connection between neurons of the first layer and neurons of the second layer, wherein the activity state of the neurons for that synaptic connection have been determined both to be active, the weight of the synaptic connection is weakened.