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AbstractEmbodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and recurrent networks , and replaces multi - layer perceptron (MLP) based sigmoidal neural structures with a queuing theory-driven design. For example, in an embodiment, a circuit may comprise a plurality of layers of neural network circuitry, each layer comprising a plurality of neuron circuits, each neuron comprising a plurality of computational circuits, and each neuron connected to a plurality of other neurons in the same layer by synapse circuitry, wherein the plurality of layers of neural network circuitry are adapted to process symbolic and conceptual information.
CitationHoward, N., Adeel, A., Gogate, M. and Hussain, A. (2019) Deep Cognitive Neural Network (DCNN). US Patent and Trademark Office application number US 20190156189A1.
PublisherUS Patent and Trademark Office
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/