Circumventing broken neural networks, both real and imaginary, through SPSF-based neural decoding and interconnected associative memory matrices

James LaRue

doi:10.1117/12.3014016

6 June 2024 Circumventing broken neural networks, both real and imaginary, through SPSF-based neural decoding and interconnected associative memory matrices

James LaRue

Author Affiliations +

Proceedings Volume 13058, Disruptive Technologies in Information Sciences VIII; 130580E (2024) https://doi.org/10.1117/12.3014016
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In previous work we have introduced our (proposed) architecture that connects a ‘Real’ and ‘Imaginary’ Neural Network. The ‘Real’ portion is represented by exploiting Striatal Beat Frequencies in an EEG with the patented Single-Period Single-Frequency (SPSF) method and the ‘Imaginary’ is represented by a convolutional neural network transformed into bi-directional associative memory matrices. We demonstrated that we could interconnect, i.e., bridge, the intermediate layers of two broken CNNs both of which were trained for object detection and still make a good prediction. In this work we will use a dual sensory CNN implementation of speech and object detection and we will incorporate Neural Decoding into the EEG SPSF method to emulate how to circumvent the broken neural networks in a human-computer interface situation.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

James LaRue "Circumventing broken neural networks, both real and imaginary, through SPSF-based neural decoding and interconnected associative memory matrices", Proc. SPIE 13058, Disruptive Technologies in Information Sciences VIII, 130580E (6 June 2024); https://doi.org/10.1117/12.3014016

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