Deep Cognitive Neural Network (DCNN)

Howard, Newton; Adeel, Ahsan; Gogate, Mandar; Hussain, Amir

Abstract

Embodiments 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.

Citation

Howard, N., Adeel, A., Gogate, M., & Hussain, A. (2019). Deep Cognitive Neural Network (DCNN). US2019/0156189

• Online Publication Date: May 23, 2019
• Publication Date: May 23, 2019
• Deposit Date: Apr 26, 2022
• Public URL: http://researchrepository.napier.ac.uk/Output/2866488
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