David J. Hamilton, PhD

PhD Neuroscience focused on computational modeling of biologically plausible neuronal circuits.

Fairfax, Virginia, United States of America

Research Expertise

Cognitive Neuroscience
Biomedical Engineering
Artificial Intelligence
Cellular and Molecular Neuroscience
Modeling and Simulation
Machine Learning

About

David J. Hamilton, PhD Neuroscience, GMU, 2016. His current research focus is Efficient Generative AI leveraging biologically plausible computational circuits and spiking neural networks to implement transformer-based algorithms. Dr. Hamilton has extensive R&D experience in Generative AI and Machine Learning capability development. Specific projects include transformer-based LLM sensor parameter tuning, analytic prediction, Cyber Threat Analysis Platform R&D, US Treasury cyber defense, credit card fraud detection, sensor fusion/analysis, LIDAR signal characterization, and active/passive sonar signal detection/classification. Companies for which David has worked include Intelligent Mission Consulting Services (2020-2023), Northrop Grumman (2004-2020), NeuralTech/CardSystems (1994-2004), Raytheon (1980-1994), and AAI (1977-1980). Earlier in his career, David received his MSEE (1981) from Loyola University, Maryland, and his BSEE (1977) from PSU. He is well published, holds memberships in Society for Neuroscience (SfN), AAAS, IEEE, and continues to maintain his association with GMU as an Affiliate Faculty.

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Publications

Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach
Journal of Pharmaceutical and Biomedical Analysis
2017
Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties
Brain Informatics
2016
An ontological approach to describing neurons and their relationships
Frontiers in Neuroinformatics
2012
Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit
Neural Networks
2008
Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus
eLife
2015
Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome
eneuro
2016
Brain Tumor Database, a free relational database for collection and analysis of brain tumor patient information
Health Informatics Journal
2015
Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences
Hippocampus
2019
Quantitative firing pattern phenotyping of hippocampal neuron types
Scientific Reports
2019
Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity
PLOS Computational Biology
2019
Quantitative firing pattern phenotyping of hippocampal neuron types
Unknown Venue
2017
Title section, volume, contents and author index, volume 32, 1992
Microelectronics Reliability
1992
Evaluation of neural network and conventional techniques for sonar signal discrimination
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering
All neural network sonar discrimination system
[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering

Education

George Mason University

Ph.D., Neuroscience / 2016

Fairfax, Virginia, United States of America

Loyola University Maryland

MS, EE / June, 1981

Baltimore, Maryland, United States of America

Penn State

BS, EE / June, 1977

State College, Pennsylvania, United States of America

Experience

George Mason University

Affiliate Faculty / October, 2023Present

Neuroscience

Intelligent Mission Consulting Services (IMCS)

Neuroscientist / July, 2020December, 2023

AI/ML Subject Matter Expert

Northrop Grumman

Neuroscience Software Engineer / July, 2004July, 2020

AI/ML Software Engineer

NeuralTech/Card Systems

VP Software Engineering / October, 1994October, 2004

Engineering Manager and AI/ML Software Engineering

Raytheon

Senior Software Engineer / October, 1980October, 1994

AI/ML Software Engineering

AAI Corp

Senior Engineer / July, 1977October, 1980

Electronic Engineering

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