Perception and Analysis lecture series
Larry Werth describes the construction and application of a system for associative pattern memory
Larry explores the basis for a computer-implementation of human-like associative pattern memory. Associative pattern memory systems have their origins in randomly connected neural network models, where terminal states end in cycles. Each state is randomly mapped to an input pattern, and the sampled input along with the current state determine the next state.
Using cycles allow for the creation of fuzzy hashing, as well as being a simple and fast implementation. It also yields a common language for pattern types, and integrates spatial and temporal information to create higher-level input patterns.
These techniques have several applications: hand-printed character recognition, machine vision, video compression, financial pattern forecasting and vector quantization in signal processing.
Larry will be releasing his software library as a general purpose tool for pattern recognition development.
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