Masking Fields: A Massively Parallel Neural Architecture for Learning, Recognizing, and Predicting Multiple Groupings of Patterned Data

摘要

A massively parallel neural network architecture, called a masking field, is characterized through systematic computer simulations. A masking field can simultaneously detect multiple groupings within its input patterns and assign activation weights to the codes for these groupings which are predictive with respect to the contextual information embedded within the patterns and the prior learning of the system. A masking field automatically rescales its sensitivity as the overall size of an input pattern changes, yet also remains sensitive to the microstructure within each pattern. Thus a masking field suggests a solution of the credit assignment problem by embodying a real-time code for the predictive evidence contained within its input patterns. Such capabilities are useful in speech recognition, visual object recognition, and cognitive information processing. An absolutely stable design for a masking field is disclosed through an analysis of the computer simulations. This design suggests how associative mechanisms, cooperative-competitive interactions, and modulatory gating signals can be joined together to regulate the learning of compressed recognition codes. Data about the neural substrates of learning and memory are compared to these mechanisms.