Staff Profiles

Jung Lee, Ph.D.

Scientist II

Jung H. Lee joined the Model, Analysis & Theory team at Allen Institute in 2014, and his research involves investigating the properties of large-scale network of point neurons. For his doctoral research, he developed neuromorphic architectures and learning algorithms suitable for CMOL CrossNets proposed by his PhD advisor, Professor Likharev. For his postdoctoral trainings, he studied neural correlates of auditory cognition at University of Pennsylvania School of Medicine and relationships between cortical rhythms and cognitive processes at Boston University. Lee received a B.S. in Physics from Yonsei University and a PhD in Physics from SUNY Stony Brook University.


Research Interests

I am interested in how neural circuits render functions necessary for cognition such as intelligence. Computational models of brain circuits would allow us to uncover the building blocks for cognition and to identify principles crucial to constructing a brain-like machine. As a member of the MAT team, I am participating in the efforts to develop computational models for mouse visual cortex and its interactions with other visual system, which would expand our understanding of visual cognition.


  • Computational neuroscience
  • Machine learning
  • Electrophysiology

Research Programs

  • Modeling, Analysis & Theory

Selected Publications View on PUBMED

DynMat, a network that can learn after learning

Neural Networks
April 8, 2019

Lee JH

Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex

September 26, 2018

Seeman SC, Campagnola L, Davoudian PA, Hoggarth A, Hage TA, Bosma-Moody A, Baker CA, Lee, JH, Mihalas S, Teeter C, Ko AL, Ojemann JG, Gwinn RP, Silbergeld DL, Cobbs C, Phillips J, Lein E, Murphy G, Koch C, Zeng H, Jarsky T

Cortical circuit-based lossless neural integrator for perceptual decision-making

March 26, 2018

Lee JH, Tsunada J, Vijayan S, Cohen YE

Top-down beta rhythms support selective attention via interlaminar interaction: a model

PLoS Computational Biology
August 8, 2013

Lee JH, Whittington MA, Kopell NJ