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.


High Throughput Connectomics

Exiguous Optogenetic Circuits

Principal Investigator

Research Scientist


David Budden
Jonathan Stoller
Gergely Odor
Victor Jakubiuk
Quan Nguyen
Robert Radway


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. 16872-16876 (October 2014).


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