Mapping the Structure of Thought
Understanding the structure and function of the nervous system is an exceptionally complex task: the system consists of thousands of cells connected to thousands of other cells in microscopic networks that extend over large volumes and exhibit a seemingly endless variety of behaviors. We believe that mapping such networks at the level of synaptic connections, and understanding the relation of their connectivity and geometry to function, will play a key role in unraveling the mystery of thought.
Our group’s goal is to create, based on such microscopic connectivity and functional data, new mathematical models explaining how neural tissue computes. Our modeling spans the connectomics gamut from the behavior of individual neurons in exiguous circuits to collections of neurons in increasingly complex networks. We collaborate with neurobiologists to design experiments based on our theoretical models, and work extensively to analyze the resulting data in order to confirm or disprove our theoretical predictions.
News
January 2017: Shavit Lab’s PPoPP 2017 paper, A Multicore Path to Connectomics-on-Demand is selected for Best Paper Nominee. June 3, 2016, MIT Commencement: Congratulations to new graduates, Gregory Odor and Hayk Saribekyan! (Pictured: Hayk Saribekyan and Professor Nir Shavit.) February 2016: The Shavit Lab has been awarded research funding under the IARPA Machine Intelligence from Cortical Networks (MICrONS) project. October 2015: Brain-like chip.
Projects
High Throughput Connectomics The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve “clusterscale” problems on a single commodity multicore machine. Connectomics is an emerging area of neurobiology that uses cutting edge machine learning and image processing
Principal Investigator
Nir Shavit
Research Scientist
Alexander Matveev
Postdoc
Yaron Meirovitch
Graduate Students
Heather Berlin Michael Coulombe Rati Gelashvili Justin Kopinsky Shibani Santurkar Hayk Saribekyan David Rolnick
Alumni
David Budden Jonathan Stoller Gergely Odor Victor Jakubiuk Quan Nguyen Robert Radway
Publications
Matveev, A., Meirovitch, Y., Saribekyan, H., Jakubiuk, W., Kaler, T., Odor, G., Budden, D., Zlateski, A., Shavit, N. A Multicore Path to Connectomics-on-Demand . PPoPP 2017 (Best Paper Nominee). Meirovitch, Y., Matveev, A., Saribekyan, H., Budden, D., Rolnick, D., Odor, G., Knowles-Barley, S., Thouis, R., Pfister, H., Lichtman, J., Shavit, N. A Multi-Pass Approach to Large-Scale Connectomics . arXiv 2017. Lichtman, J., Pfister, H., and Shavit, N. The big data challenges of connectomics. Nature Neuroscience, 17, pp. 1448-1454 (November 2014). Zeyuan Allen-Zhu, Rati Gelashvili, Silvio Micali, and Nir Shavit. Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints. Proceedings of the National Academy of Sciences USA; 111(47), pp.
Resources
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