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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

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Artificial neural networks using complex numbers and phase encoded weights.

Howard E Michel¹, Abdul Ahad S Awwal

¹Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, Massachusetts 02747, USA. hmichel@umassd.edu

|April 2, 2010

Summary

This summary is machine-generated.

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A novel complex-valued neuron (CVN) model significantly enhances problem-solving capabilities compared to traditional perceptrons. This advanced model offers superior performance in Boolean logic and computer vision tasks, even in optical implementations.

Area of Science:

Computational Neuroscience
Artificial Intelligence
Optical Computing

Background:

Traditional perceptrons have limitations in solving complex linearly separable problems.
Existing methods to enhance perceptron capabilities often require additional complexity or resources.

Purpose of the Study:

To propose and evaluate a novel complex-valued neuron (CVN) model.
To demonstrate the CVN's superiority over traditional perceptrons in computational tasks.
To explore the CVN's suitability for optical implementation and its cost-effectiveness.

Main Methods:

Developed a perceptron model utilizing phase-encoded inputs and complex-valued weights.
Derived aggregation, activation, and learning functions for the complex-valued neuron (CVN).

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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

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Published on: December 15, 2023

Applied the CVN to Boolean logic functions and computer vision tasks, including character recognition and image segmentation.

Main Results:

The CVN achieved a 135% improvement over the theoretical maximum for solvable linearly separable problems.
Demonstrated superior performance in distortion-invariant character recognition and image segmentation.
CVN offers cost benefits, especially in optical implementations due to the natural complexity of optical computations.

Conclusions:

The complex-valued neuron (CVN) model presents a significant advancement over traditional perceptrons.
CVN provides enhanced computational power without increased structural complexity.
The CVN is particularly advantageous for optical computing, offering performance gains at a reduced or comparable cost.