Patent ID: 9183494
Filing Date: 2015-11-10
Classification: G06N

Abstract:
1. A computer-implemented method for implementing an artificial neural system including an input layer configured to respond to stimulus, a set of neurons and a set of connections between the input layer and the set of neurons, the method comprising: determining a response for individual ones of the set of neurons with respect to a feature in the stimulus, wherein the response correlates positively to a frequency of the feature in the stimulus; comparing responses for neurons in the set of neurons with respect to the feature in the stimulus; identifying, from the set of neurons, a maximally responding neuron, the maximally responding neuron being a neuron having a larger response to the feature in the stimulus than all other neurons in the set of neurons; updating weights of individual connections of the maximally responding neuron according to a first learning rule, wherein the first learning rule uses a first function based on a first modification threshold; updating weights of individual connections of the other neurons in the set of neurons according to a second learning rule, wherein the other neurons are different from the maximally responding neuron, wherein the second learning rule uses a second function based on a second modification threshold, wherein a value of the second modification threshold for a particular individual other neuron is greater than the response of the particular individual other neuron to the feature in the stimulus and the value of the second modification threshold is obtained by multiplying the response of the particular individual other neuron by a factor larger than one; and generating responses to the feature in the stimulus by the maximally responding neuron and other neurons respectively according to their respective updated weights, wherein application of the first learning rule strengthens the response of the maximally responding neuron to the feature in the stimulus and wherein application of the second learning rule weakens the response of the other neurons to the feature in the stimulus.