David Hamilton


Senior Software Engineer, Northrop Grumman Neuroscientist, PhD from George Mason University


LinkedIn page:  https://www.linkedin.com/pub/david-hamilton/38/91/5aa

Email:  david.j.hamilton@ngc.com
Address: 22030


George Mason University

PhD Candidate, Neuroscience

Estimated time-frame for dissertation final defense: Summer 2016.
Dissertation title: “Machine-readable Knowledge Management of Neuron Properties.”

Activities and Societies: Society for Neuroscience, AAAS, IEEE

Loyola College in Maryland

MS, Electrical Engineering 

Penn State University

BS, Electrical Engineering

Work Experience

Neuroscience PhD Candidate

George Mason University  – Present (8 years)Fairfax, VAExpect to defend Spring 2016

Software Engineer

Northrop Gruman Information Systems – – Present (11 years 2 months)

Software & Systems Research & Development

VP Software Development NeuralTech  (9 years 8 months)
Merchant Dispute and Fraud Detection Software System Development

Senior Engineer

Raytheon   (14 years 1 month)CAD and ANN Software System Development


AAI Corporation  (3 years 3 months)ATE Hardware Design and Software Development

Article in AAAS Spotlight

November 26, 2012

David Hamilton
Cyber software engineering manager, Northrop Grumman Corporation

Background: I am a cyber software engineering manager working for Northrop Grumman Corporation in Northern Virginia. While working fulltime, I am also a part-time neuroscience Ph.D. student at George Mason University, having achieved candidacy and expecting to defend in 2015.

Question 1: Why did you become a researcher/engineer/scientist?
Answer: I like building things to solve problems. This is what engineers do. So it was a natural fit for me to become an engineer. My foray into scientific research came about with my continued pursuit of potential solutions to problems that required ever more advanced intellectual abilities to realize automaton. The brain is a logical place to look for good examples of automatous intelligent capability. So, I am now a Neuroscience Ph.D. candidate (i.e. an engineer studying the brain).

Question 2: What fuels your passion for your work?
Answer: Being relevant is important to me. One of the most satisfying moments in my career was learning, about a year after I had moved on to a different project, that a software system I had written was still being use heavily and had been incorporated into a standard engineering design and development process. It was very satisfying to have significantly contributed to that particular workflow. I am motivated to see the fruits of my labor be relevant.

Question 3: Tell us why you chose your particular field of study, why did it grab your interest and fuel your curiosity?
Answer: Neuroscience is the most interesting and potentially useful field of study available to me at this stage in my career. I was trained as an electrical engineer, worked most of my life as a software engineer, but desire to learn how the brain works to glean useful architectural aspects for continued advancement in problem solving.

Question 4: Tell us about a hobby or passion outside of work.
Answer: My passion outside of work is music. I have been playing bass guitar for most of my life and have recently graduated to double bass. In high school, I played trumpet in the marching band, bass guitar in the concert band, and played bass in many rock bands. I continue to jam with others as often as possible. Jazz is my favorite form of music, but I appreciate all types of music.

Question 5: If you were president for a day, what would be the first law you would want to pass?
Answer: Term limits and flat tax.


My research is focuses on conceptualizing a cognitive processor inspired by evolutionarily prescribed biological systems. The hippocampal formation computational component of this processor is based on biologically realistic circuitry derived from Hippocampome.org (Wheeler et al., 2015). This highly curated open-access resource defines rodent hippocampal neuron types primarily based on axonal-dendritic patterning and is informed through dense coverage of peer-reviewed literature. With the availability of Hippocampome.org (basis of recently defended dissertation work, a portion of which is described in Hamilton et al., 2016), it is now possible to implement the Hippocampal Spiking Neural Network (HSNN) with biological analogy at the cellular circuitry level. We propose performing HSNN simulations within the operational context of a cognitive container to facilitate enhanced machine learning.


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

By D. J. Hamilton, D. W. Wheeler, C. M. White, C. L. Rees, A. O. Komendantov, M. Bergamino, G. A. Ascol
Brain Informatics | June 9, 2016


Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. Hippocampome.org is a web-accessible neuroinformatics resource that organizes existing data about essential properties of all known neuron types in the rodent hippocampal formation. Hippocampome.org links evidence supporting the assignment of a property to a type with direct pointers to quotes and figures. Mining this knowledge from peer-reviewed reports reveals the troubling extent of terminological ambiguity and undefined terms. Examples span simple cases of using multiple synonyms and acronyms for the same molecular biomarkers (or other property) to more complex cases of neuronal naming. New publications often use different terms without mapping them to previous terms. As a result, neurons of the same type are assigned disparate names, while neurons of different types are bestowed the same name. Furthermore, non-unique properties are frequently used as names, and several neuron types are not named at all. In order to alleviate this nomenclature confusion regarding hippocampal neuron types and properties, we introduce a new functionality of Hippocampome.org: a fully searchable, curated catalog of human and machine-readable definitions, each linked to the corresponding neuron and property terms. Furthermore, we extend our robust approach to providing each neuron type with an informative name and unique identifier by mapping all encountered synonyms and homonyms.


Hippocampus Neuron Type Property Nomenclature

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