Ph.D. in Neuroscience Research Assistant in the Center for Neural Informatics, Neural Structures, and Neural Plasticity (CN3) Curator of the Neuroscience Knowledge Network (NKN)

Todd’s dissertation was titled “Comparative topological analysis of neuronal arbors via sequence representation and alignment“. In addition to being the lead curator for NKN, Todd is overseeing user experience development for the onAir Knowledge Network System. Todd also will be participating in the Allen Institute Big Neuron hackathon this June at Janelia.

Web Information

Webpage: tgillette.onair.cc/ CN3 website: krasnow1.gmu.edu/cn3/

Contact Information

Email: todd@onair.cc GMU email: tgillett@gmu.edu Address: Northern Virginia

Biography

BS Engineering and Computer Science, Swarthmore (1999-2003) PhD Neuroscience, George Mason University (2006-2015)

Neuroscience Research Assistant with extensive experience in software development, data management, statistics, data visualization, and bioinformatics. Current research involves bioinformatic pattern searching applied to neuronal morphology. Future aim is to study neuronal networks and their specific information processing roles and capabilities. Interests also include science policy and educational outreach.

Todd studied Engineering and Computer Science at Swarthmore College, graduating in 2003, after which he moved to Virginia to work in IT (specifically knowledge managements systems) with Vivakos Inc until 2006. He then entered the Neuroscience PhD Program at George Mason University with a 2-year ...

The sequence representation presented in the preceding chapter can be used to detect clusters of similar neurons which can then be investigated for their stereotypical branching patterns. We use a modified sequence alignment procedure to produce pairwise similarity values between two neuronal sequences and then, after embedding the neurites (axons, dendrites, or apical dendrites) into a space consistent with those values, look for clusters in the space. The clusters are then overlayed with known cell classes to see whether significant associations emerge. The results indeed show a number of very strong associations by cell type, brain region, and species, indicating that neuronal branching patterns are indicative of neuronal type and thus function. We further explore the particular prototypical structures of each cluster and comment on the drivers of those structures and their significance to function, primarily in terms of connectivity. Three figures from the chapter (and the journal article) are shown below.

Growth or retraction of a branch is associated with specific changes in the trees sequence representation. These processes are used to adapt sequence alignment for sequences derived from trees.

The space created from alignment scores of axons ...

This year Hanchuan Peng (Allen Institute for Brain Science) began the next phase of the effort called Big Neuron. However, rather than a competition, this time the project would be a collaboration.

The key idea is to create a single platform on which all algorithms can be run, compared, and their results combined to form reconstructions better than any one could achieve alone.

From Allen Institute for Brain Science’s Big Neuron

Several years ago a joint project was started in order to determine just what the state of the art was in automatically generating digital reconstructions of neurons. The effort stands between computer science, specifically machine learning and computer vision, and neuroscience. The neuronal reconstructions are currently used for various morphological analyses and computational modeling, helping scientists better understand the relationships between morphology, electrophysiology, gene and protein networks, and disease states. In the future, it could also be highly informative for understanding the connectivity within and between brain regions which underlies neural computation and ultimately how our minds work. Producing a single reconstruction can take many hours, even days or weeks depending on the size and complexity, which keeps the field from being able to exploit big data approaches and making discoveries it would ...

My dissertation, entitled “Comparative topological analysis of neuronal arbors via sequence representation and alignment”, is focused on applying bioinformatic approaches to neuronal morphology to enable new discoveries and increase understanding about how morphology and neuron function interrelate.

Chapter 1: An outline of my dissertation. Neuronal morphology interacts with many other features of neurons and their networks. As more data becomes available through centralized curation of existing and new data through NeuroMorpho.Org, and as several fold increases of data production occur with development of high-throughput imaging and reconstruction techniques, new questions become possible. While chapter 2 provides background on the field of neuronal morphology, chapter 3 discusses an effort to aid in the development of automated reconstruction algorithms. Chapters 4 and 5 introduce techniques based on graph theory and genomics, starting with a representation of morphologies as sequences of branch features and followed by scalable analyses seeking representative morphological patterns that can allow for testing and refinement of functional and developmental hypotheses.

Chapter 2: The second chapter provides a background into known relationships between morphology and function, particularly focused on but not limited to dendrites, as well as the various measurement, experimentation, and modeling methods that have been produced ...

IBM has recently released details of a neuromorphic chip named TrueNorth via their website, the press, and a research report in the journal Science. The research team, headed by Dharmendra Modha as part of the DARPA SyNAPSE Program, developed a chip containing a million “programmable spiking neurons” and 256 million synapses. The chips use 5.4 billion transistors on 4096 “neurosynaptic cores” which each has its memory (in the form of connection routing and timing delays) close to its “neuronal” processing units. This architecture, including plasticity rules, enables pattern learning and recognition in a manner broadly similar to brains and far superior to current von Neumann architectured computers given tiny fraction of power used (20 microwatts/cm2 vs 50-100 watts/cm2). This power saving is enabled by using an event-driven asynchronous design. Moreover, the synaptic connections can be exported and imported, allowing for learned expertise to be shared once learned.

The research article primarily focuses on architecture and benchmarks of speed and energy use. The authors additionally provide an example application. “The task had two challenges: (i) to detect people, bicyclists, cars, trucks, and buses that occur sparsely in images while minimizing ...

While this latest research is a proof of concept, it’s a pretty impressive and important one. Scientists started out putting stem cells into mouse brains that were genetically modified to produce less myelin, essentially an electrical insulator of neuronal connections, and thus have various cognitive and motor deficits (abstract). Similar problems plague people through various demyelination diseases of the central nervous system (CNS), like multiple sclerosis, or of the peripheral nervous system (PNS), like Guillain-Barré syndrome. New cell growth was detected in the mouse brains, with a great proportion of the new cells being oligodendrocytes which are the cells responsible for covering neuronal connections with myelin (in the CNS). Substantial additional myelination was also observed with no clear negative effects.

Following these results the scientists ran a case study (abstract) on 4 boys with the genetic disorder Pelizaeus-Merzbacher disease which produces defective oligodendrocytes. The scientists injected stems cells, along with immunosuppression drugs, into the boys and saw substantial improvement in myelination near the implantation sites with no apparent adverse effects. Future studies will involve more patients and hopefully will lead to treatments of a number of highly debilitating diseases.

For another review ...

When one ponders the possibilities of the future of robotics and “artificial” intelligence (perhaps more properly “synthetic intelligence” once we’ve actually got it figured out), one usually looks to hard science fiction books. Isaac Asimov in particular comes to mind. A few movies have done an ok job, including the interesting but also very flawed “I, Robot” with Will Smith and Bridget Moynahan, and Terminator: Salvation. Both were at least decent movies, but neither felt particularly realistic and both tried to pull on our emotions in fairly obvious ways that felt anything but organic (the irony of that word is not lost on me).

I guess I haven’t been keeping up on the sci-fi production news recently, because the preview for “Automata” caught me completely by surprise. Starring Antonio Banderas, here we have a believable future (2044, 30 years from now) in which desertification is threatening society, and a single company is leading the way in intelligent robotics. Will this happen? Maybe not, but could it happen? Certainly. There’s at least one nod to Asimov’s 3 laws, and at least from the preview it feels more like a Asimov story, albeit with a somewhat gloomier tone, than “I, ...

This book is surprisingly good in its ability to reach both the lay reader (for at least the first half) and the reader familiar with neuroscience. Articles since its publications provide much greater detail and are very useful for those interested in going deeper, but On Intelligence serves very well as an introduction to the concepts. The ideas expressed in On Intelligence are important both for scientific advancement and for philosophical consideration. While one could argue that perhaps there are other forms of intelligence or ways to produce intelligence, Hawkins does a good job in arguing what intelligence is in terms of mammalian brains and what the basic neocortical unit does. While Hawkins brings these ideas together in an orderly framework, he does give credit to the many neuroscientists responsible for the various components and underlying ideas that make it possible. These ideas as a whole until recently have not been sufficiently discussed in the neuroscience community in my opinion, and I believe they will aid (and in fact already have aided) greatly in advancing our understanding of the brain and creating real “artificial” intelligence that isn’t actually artificial at all.

The balance between addressing the expert and lay audiences did at ...

Analysis of neuronal morphology has until recently been limited to small data sets due to the amount of time necessary to generate a digitized representation of a neuron’s structure. Recent efforts to curate data from many different labs working in parallel on vastly differing topics has led to the NeuroMorpho.Org database which currently provides access and metadata for over 10,000 neurons from a variety of species, cell types, brain regions, and experimental conditions. No one would call 10,000 “Big Data”, but that number is expected to rise dramatically in the future. These curation efforts have been growing more successful as data sharing has slowly transitioned to be the exception to the norm, a literature mining effort has taken off, and reconstruction techniques, including semi-automatic and fully-automatic reconstruction, are advancing rapidly.

When a type of data grows this way, new analysis techniques and technology are required to take advantage of it. When the human genome project was happening, the data growth was also enormous, and the field of sequence analysis became vital. A variety of different analyses were developed with heuristics specific to the data to make data processing fast and effective. When considering neuronal data, it is fair to ask whether such ...

Mason News Dec. 10, 2012 Catherine Probst

Alice Quatrochi, BIS ’12 Fine Arts and Educational Psychology; Todd Gillette, PhD student in neuroscience; and Giorgio Ascoli, University Professor of Molecular Neuroscience, work on the “Mental Floss” sculpture. Photo by Evan Cantwell

Artists and scientists approach creativity, exploration and research in different ways and from different perspectives. But when people from these disparate fields work together, they have the potential to create new knowledge, ideas and processes that help us see the world around us in a different light.

This is exactly what several Mason faculty and students set out to accomplish when they created an interpretive 3-D sculpture called “Mental Floss.” The sculpture depicts 13 different neurons that are located in the hippocampus, the region of the brain responsible for processing autobiographical memories.

Funded by Mason’s Center for Consciousness and Transformation, the project originated as a science+art exchange project organized under an umbrella initiative called the SOFAlab. SOFAlab is collaboratively administrated by Mason’s School of Art; the Smith Center for Healing and the Arts; and the Hamiltonian Artists, which was founded by Paul So, associate professor in the School of Physics, Astronomy, and Computational Sciences and the Krasnow Institute for ...