Team

Staff Profiles

Associate Investigator

Uygar Sümbül joined the Allen Institute as an Assistant Investigator in 2017. His current research at the Institute focuses on the modeling and analysis of neuronal cell type identity based on genetic, anatomical, and physiological data. Previously, Uygar held postdoctoral positions in Sebastian Seung’s lab at MIT, and Liam Paninski’s lab at Columbia University. During these appointments, he developed machine learning methods for multi-modal classification of cell types, and for segmenting individual neurons in multispectral images of the nervous tissue. In particular, his research demonstrated a submicron reproducibility in the anatomy of mouse retinal neurons, and the existence of a consistent mapping between anatomical and molecular definitions of cell types. Uygar received a Bachelor’s degree from Bilkent University (Ankara, Turkey). He earned his PhD in Electrical Engineering and Mathematics from Stanford University, where he studied time-series models of dynamic magnetic resonance imaging.

Research Interests

What defines a neuronal cell type is a long-standing problem in Neuroscience. It is not always clear whether consistent identities can be assigned to neuronal populations across anatomical, molecular, and physiological observations. Uygar’s research tries to address these problems using the multi-modal observations of cortical neurons obtained at the Institute. He develops machine learning methods for classification that take advantage of these large, high-quality datasets. Uygar is also broadly interested in the anatomical organization of the cortex and how this pertains to neuronal identity.

Expertise

Computational neuroscience
Machine learning
Neuroanatomy

Research Programs

MAT
Cell type classification

Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types

Cell Reports
August 9, 2022

Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA

Cell-type-specific neuromodulation guides synaptic credit assignment in a spiking neural network

Proceedings of the National Academy of Sciences
December 21, 2021

Liu YH, Smith S, Mihalas S, Shea-Brown E, Sümbül U

New light on cortical neuropeptides and synaptic network plasticity

Current Opinion in Neurobiology
August 2020

Smith SJ, Hawrylycz M, Rossier J, Sümbül U

Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Dendritic and Somatic Spiking

Neuron
March 12, 2020

Iascone DM, Li Y, Sümbül U, Doron M, Chen H, Andreu V, Goudy F, Blockus H, Abbott LF, Segev I, Peng H, Polleux F

Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells

Cell
November 12, 2020

Gouwens NW, Sorensen SA, Baftizadeh F, Budzillo A, Lee BR, Jarsky T, Alfiler L, Baker K, Barkan E, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Crichton K, Daigle TL, Dalley R, de Frates RA, Dee N, Desta T, Lee SD, Dotson N, Egdorf T, Ellingwood L, Enstrom R, Esposito L, Farrell C, Feng D, Fong O, Gala R, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Graybuck L, Gu H, Hadley K, Hawrylycz MJ, Henry AM, Hill D, Hupp M, Kebede S, Kim TK, Kim L, Kroll M, Lee C, Link KE, Mallory M, Mann R, Maxwell M, McGraw M, McMillen D, Mukora A, Ng L, Ng L, Ngo K, Nicovich PR, Oldre A, Park D, Peng H, Penn O, Pham T, Pom A, Popović Z, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Robertson M, Ronellenfitch K, Ruiz A, Sandman D, Smith K, Sulc J, Sunkin SM, Szafer A, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Ward K, Williams G, Zhou Z, Ting JT, Arkhipov A, Sümbül U, Lein ES, Koch C, Yao Z, Tasic B, Berg J, Murphy GJ, Zeng H

Transcriptomic evidence for dense peptidergic neuromodulation networks in mouse cortex

bioRxiv
January 13, 2019

Smith SJ, Sumbul U, Graybuck LT, Collman F, Sharmishtaa S, Gala R, Gliko O, Elabbady L, Miller J, Bakken T, Yao Z, Lein ES, Zeng H, Tasic B, Hawrylycz M

Single-cell transcriptomic evidence for dense intracortical neuropeptide networks.

eLife
November 11, 2019

Smith SJ, Sümbül U, Graybuck LT, Collman F, Seshamani S, Gala R, Gliko O, Elabbady L, Miller JA, Bakken TE, Rossier J, Yao Z, Lein E, Zeng H, Tasic B, Hawrylycz M

The Markov link method: a nonparametric approach to combine observations from multiple experiments

bioRxiv
October 30, 2018

Loper J, Bakken T, Sumbul U, Murphy G, Zeng H, Bleib D, Paninski L

Automated scalable segmentation of neurons from multispectral images

Advances in Neural Information Processing Systems
2016

Sümbül U, Roossien D, Cai D, Chen F, Barry N, Cunningham JP, Boyden E, Paninski L

Neuronal cell types and connectivity: lessons from the retina

Neuron
September 17, 2014

Seung HS, Sümbül U

A genetic and computational approach to structurally classify neuronal types

Nature Communications
March 24, 2014

Sümbül U, Song S, McCulloch K, Becker M, Lin B, Sanes JR, Masland RH, Seung HS