Adaptive Resonance Theory

The prime perception after the Adaptive resonance theory model is that entity recognition usually occurs as a result of the interface of ‘top-down’ observer expectations with ‘bottom-up’ sensory information. Adaptive resonance theory is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It explain a number of neural network models which use managed and unsupervised learning techniques and address troubles such as pattern gratitude and forecast.