Name: Looking Inside the Blackbox Anatomies of Real and Imaginary Neural Networks
Start: 2025-12-14T10:00:00-05:00
End: 2025-12-14T12:00:00-05:00

Title: Looking Inside the Blackbox Anatomies of Real and Imaginary Neural Networks
Date: December 14 2025 10:00 am - Noon EST
Summary:
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.
Reference: [https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13058/130580E/Circumventing-broken-neural-networks-both-real-and-imaginary-through-SPSF/10.1117/12.3014016.short](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13058/130580E/Circumventing-broken-neural-networks-both-real-and-imaginary-through-SPSF/10.1117/12.3014016.short "Original URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13058/130580E/Circumventing-broken-neural-networks-both-real-and-imaginary-through-SPSF/10.1117/12.3014016.short. Click or tap if you trust this link.")

Speaker:
Dr. James LaRue has been a scientist/consultant for over 20 years. He has multiple publications. He earned his PhD in Engineering/Applied Science from University of New Orleans in 2003. He got his Masters in Math from Tulane University in 1995. He got his BA in Math/Education in 1992. But it all started out with an AAS in Instrumentation Electronics Technology at Monroe Community College, Rochester, NY in 1979.
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Looking Inside the Blackbox Anatomies of Real and Imaginary Neural Networks

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