David J. Hamilton, PhD

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

Fairfax, Virginia, United States of America

Research Interests

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.

Publications

Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach

Journal of Pharmaceutical and Biomedical Analysis / Sep 01, 2017

Hamilton, D. J., White, C. M., Rees, C. L., Wheeler, D. W., & Ascoli, G. A. (2017). Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach. Journal of Pharmaceutical and Biomedical Analysis, 144, 269–278. https://doi.org/10.1016/j.jpba.2017.03.062

Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties

Brain Informatics / Jun 09, 2016

Hamilton, D. J., Wheeler, D. W., White, C. M., Rees, C. L., Komendantov, A. O., Bergamino, M., & Ascoli, G. A. (2016). Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties. Brain Informatics, 4(1), 1–12. https://doi.org/10.1007/s40708-016-0053-3

An ontological approach to describing neurons and their relationships

Frontiers in Neuroinformatics / Jan 01, 2012

Hamilton, D. J., Shepherd, G. M., Martone, M. E., & Ascoli, G. A. (2012). An ontological approach to describing neurons and their relationships. Frontiers in Neuroinformatics, 6. https://doi.org/10.3389/fninf.2012.00015

Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit

Neural Networks / Oct 01, 2008

Scorcioni, R., Hamilton, D. J., & Ascoli, G. A. (2008). Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit. Neural Networks, 21(8), 1153–1163. https://doi.org/10.1016/j.neunet.2008.05.006

Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus

eLife / Sep 24, 2015

Wheeler, D. W., White, C. M., Rees, C. L., Komendantov, A. O., Hamilton, D. J., & Ascoli, G. A. (2015). Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus. ELife, 4. CLOCKSS. https://doi.org/10.7554/elife.09960

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome

eneuro / Nov 01, 2016

Rees, C. L., Wheeler, D. W., Hamilton, D. J., White, C. M., Komendantov, A. O., & Ascoli, G. A. (2016). Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome. Eneuro, 3(6), ENEURO.0205-16.2016. https://doi.org/10.1523/eneuro.0205-16.2016

Brain Tumor Database, a free relational database for collection and analysis of brain tumor patient information

Health Informatics Journal / Mar 01, 2015

Bergamino, M., Hamilton, D. J., Castelletti, L., Barletta, L., & Castellan, L. (2015). Brain Tumor Database, a free relational database for collection and analysis of brain tumor patient information. Health Informatics Journal, 21(1), 36–45. https://doi.org/10.1177/1460458213496661

Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences

Hippocampus / Oct 09, 2019

White, C. M., Rees, C. L., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2019). Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences. Hippocampus, 30(5), 472–487. Portico. https://doi.org/10.1002/hipo.23165

Quantitative firing pattern phenotyping of hippocampal neuron types

Scientific Reports / Nov 29, 2019

Komendantov, A. O., Venkadesh, S., Rees, C. L., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2019). Quantitative firing pattern phenotyping of hippocampal neuron types. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-52611-w

Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity

PLOS Computational Biology / Oct 28, 2019

Venkadesh, S., Komendantov, A. O., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2019). Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity. PLOS Computational Biology, 15(10), e1007462. https://doi.org/10.1371/journal.pcbi.1007462

Quantitative firing pattern phenotyping of hippocampal neuron types

Oct 31, 2017

Komendantov, A. O., Venkadesh, S., Rees, C. L., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2017). Quantitative firing pattern phenotyping of hippocampal neuron types. https://doi.org/10.1101/212084

Title section, volume, contents and author index, volume 32, 1992

Microelectronics Reliability / Dec 01, 1992

Title section, volume, contents and author index, volume 32, 1992. (1992). Microelectronics Reliability, 32(12), i–xiv. https://doi.org/10.1016/0026-2714(92)90425-k

Evaluation of neural network and conventional techniques for sonar signal discrimination

[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering

Pridham, R. G., & Hamilton, D. J. (n.d.). Evaluation of neural network and conventional techniques for sonar signal discrimination. [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering. https://doi.org/10.1109/icnn.1991.163360

All neural network sonar discrimination system

[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering

Cottle, D. W., & Hamilton, D. J. (n.d.). All neural network sonar discrimination system. [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering. https://doi.org/10.1109/icnn.1991.163322

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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