Spann: sequence processing artificial neural network

Abstract

An artificial neural network is provided using a modular, self-organizing approach wherein a separate neural field is contained within each module for recognition and synthesis of particular characteristics of respective input and output signals thereby allowing several of these modules to be interconnected to perform a variety of operations. The first output and second input of one module is respectively coupled to the first input and second output of a second module allowing each module to perform a bi-directional transformation of the information content of the first and second input signals for creating first and second output signals having different levels of information content with respect thereto. In the upward direction, the first low-level input signal of each module is systematically delayed to create a temporal spatial vector from which a lower frequency, high-level first output signal is provided symbolic of the incoming information content. Since the first output signal contains the same relevant information as the first input signal while operating at a lower frequency, the information content of the latter is said to be compressed into a first high-level output signal. In the downward direction, a second output signal having a low-level of information content is synthesized from a second input signal having a high-level of information content. The second input signal is the best prediction of the first output signal available from the knowledge base of the module, while similarly the second output signal is the prediction of the first input signal.