ART

Adaptive resonance theory or ART is a theory developed by Stephen Grossberg and Gail carpenter. It is based on how brain processes information.  It is generally used in the context of pattern recognition and prediction. Prediction/PR occurs as a result of interaction of top down observers expectations with bottom up sensory information i.e. the information obtained about an entity as detected by your senses is compared with the prototype or memory template of the expectation. If the difference does not exceed a threshold value, the sensed object is considered to be a member of the expected class. Thus it does not affect the plasticity/stability of the existing knowledge.

The primitive ART model is an unsupervised model. It consists of a comparison field and a recognition field composed of neurons, a vigilance parameter (threshold value) and a reset module. The comparison field takes an input vector and transfers it to its best match in recognition field. The best match refers to a single neuron whose weight vectors closely matches the input vector. The other neurons of the recognition field exhibits lateral inhibition by sending out negative signal and as a result the best match neuron is allowed to represent a category to which input vectors are classified.

After the input vector is classified, the reset module compares the strength of the recognition field to the vigilance threshold. If the threshold is overcome, the recognition field neuron is adjusted towards the input vector. Otherwise, if the strength gauged by the reset module is below the threshold, the winning neuron is inhibited and a search procedure is carried out. In this search procedure, the recognition field neurons are inhibited one by one until the vigilance parameter is overcome. If no such neuron of the recognition field overcomes the vigilance parameter, then an uncommitted neuron is committed and its weights are adjusted towards matching the input vector.  The quality of the memory is directly proportional to the vigilance threshold.

Thus, ART is Artificial Neural Network system that must be able to adapt to changing environment and a potential solution for the Plasticity/Stability dilemma.

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