Please forward this error screen to 104. Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of neural network models which use supervised and unsupervised learning methods, and address art in theory pdf such as pattern recognition and prediction. The model postulates that ‘top-down’ expectations take the form of a memory template or prototype that is then compared with the actual features of an object as detected by the senses.
This comparison gives rise to a measure of category belongingness. As long as this difference between sensation and expectation does not exceed a set threshold called the ‘vigilance parameter’, the sensed object will be considered a member of the expected class.
The basic ART system is an unsupervised learning model. In this way the recognition field exhibits lateral inhibition, allowing each neuron in it 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 match to the vigilance parameter.
In this search procedure, recognition neurons are disabled one by one by the reset function until the vigilance parameter is overcome by a recognition match. If no committed recognition neuron’s match overcomes the vigilance parameter, then an uncommitted neuron is committed and its weights are adjusted towards matching the input vector. There are two basic methods of training ART-based neural networks: slow and fast.