I have been interested in science and, in particular, neuroscience, for a long time. I participated in the 2003 International Chemistry Olympiad, as well as won the Canadian National Biology Competition (in 2002 and 2003). At the age of 13, I also competed in, and won, the 2001 International Brain Bee, a high school level competition focused on modern advances in neuroscience. This led me to work in a research lab, while in high school; I worked at Toronto's Samuel Lunenfeld Research Institute (SLRI) with Professor Sabine Cordes, studying the serotonergic system in a mouse model of hyperactivity.
During my undergraduate studies, I pursued a number of different research opportunities, while obtaining a double major in Chemical Physics and Biochemistry, with a minor in Mathematics from the University of Toronto. I split my research work between both experimental and theoretical groups. I worked with Professor Katherine Siminovitch characterizing the role of an actin organizing protein, N-WASp, in mouse brain-specific conditional knockouts. Concurrently, I also worked with Professor Paul Brumer exploring theoretical problems in quantum mechanics, focusing on the control of system dynamics.
In my third year, I worked at Janelia Farm in Dr. Karel Svoboda’s lab, training mice in a learned behavior in order to gain insight into the neural coding of somatosensation in the barrel cortex. I then focused on computational learning networks, for my fourth year thesis; I worked on restricted Boltzmann Machine neural networks, under Professor Geoffrey Hinton, studying the results of modifying these models to make them more biologically realistic.
Currently, as a student in the joint Janelia Farm-Cambridge University graduate program, my supervisors are Professor Simon Laughlin (Cambridge), and Mitya Chklovskii (JFRC). Our aim is to apply theoretical techniques to gain an understanding of the function of different subsets of neurons within the fly visual system.
By understanding the theoretical structure of the computations performed in the visual system, we should be able to constrain the potential circuit structures that can be used to implement these computations. Then, by looking for these topologies, it should be possible to associate functions with particular neurons within the fly visual system.
The two computations that I have focused on are mean adaptation, and motion detection. In the case of mean adaptation, we have been able to show that only a limited number of small neuronal circuits can implement mean adaptation. In the case of motion detection, our theoretical analysis has allowed us to provide alternative models to the existing (well-accepted) Elementary Motion Detection (EMD) model. These alternatives have more natural implementations than the EMD within the circuitry of the fly visual system.
In addition to these theoretical results, I have been developing a novel measure of error probability in semi-automated electron microscopy (EM) reconstruction, giving us the probability of a single proofreader’s work resulting in an error within the final connectivity diagram. We have used these probabilities to drive a new method of proofreading EM reconstructions, which allows us to provide probability bounds on the resulting connectivity matrix, while also allowing us to speed up the proofreading process. This method of synapse-driven reconstruction is currently being implemented, at Janelia, in semi-automated reconstruction of a column from the fly medulla.
Finally, I have also been working on a computationally efficient biophysical model of synaptic transmission. This should allow Monte Carlo simulations of cellular connections within the fly visual system, which often contain many tens of synapses. These simulations should provide a better understanding of the way activity is transmitted in the system, allowing us to better constrain the function of individual neurons within the fly visual system.
Outside the lab, I enjoy spending time in the great outdoors: hiking, bird-watching, and photographing nature. I also like to fit in a game of ultimate Frisbee, tennis, or soccer. In addition, I try to find some time to go scuba diving, as well as flying (having gotten my private pilot’s license, just before starting my graduate work).
Large-scale automated histology in the pursuit of connectomes.The Journal of Neuroscience : the Official Journal of the Society for Neuroscience 2011
D. Kleinfeld, A. Bharioke, P. Blinder, D. D. Bock, K. L. Briggman, D. B. Chklovskii, W. Denk, M. Helmstaedter, J. P. Kaufhold, W. Lee, H. S. Meyer, K. D. Micheva, M. Oberlaender, S. Prohaska, C. R. Reid, S. J. Smith, S. Takemura, P. S. Tsai, and B. Sakmann The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 31:16125-16138 (2011)
How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain's computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.
Prior Publications (3)
N-WASp is required for Schwann cell cytoskeletal dynamics, normal myelin gene expression and peripheral nerve myelination.Development (Cambridge, England) 2011
F. Jin, B. Dong, J. Georgiou, Q. Jiang, J. Zhang, A. Bharioke, F. Qiu, S. Lommel, L. M. Feltri, L. Wrabetz, J. C. Roder, J. Eyer, X. Chen, A. C. Peterson, and K. A. Siminovitch Development (Cambridge, England), 138:1329-37 (2011)
Schwann cells elaborate myelin sheaths around axons by spirally wrapping and compacting their plasma membranes. Although actin remodeling plays a crucial role in this process, the effectors that modulate the Schwann cell cytoskeleton are poorly defined. Here, we show that the actin cytoskeletal regulator, neural Wiskott-Aldrich syndrome protein (N-WASp), is upregulated in myelinating Schwann cells coincident with myelin elaboration. When N-WASp is conditionally deleted in Schwann cells at the onset of myelination, the cells continue to ensheath axons but fail to extend processes circumferentially to elaborate myelin. Myelin-related gene expression is also severely reduced in the N-WASp-deficient cells and in vitro process and lamellipodia formation are disrupted. Although affected mice demonstrate obvious motor deficits these do not appear to progress, the mutant animals achieving normal body weights and living to advanced age. Our observations demonstrate that N-WASp plays an essential role in Schwann cell maturation and myelin formation.
Quantum conditions on the control of dynamics of a system coupled to an environment are obtained. Specifically, consider a system initially in a system subspace H(0) of dimensionality M(0), which evolves to populate system subspaces H(1), H(2) of dimensionalities M(1), M(2). Then, there always exists an initial state in H(0) that does not evolve into H(2) if M(0)>dM(2), where 2