Assistant Professor of Neural Science, NYU Shanghai
Sukbin Lim is an assistant professor of neural and cognitive sciences at NYU Shanghai. She obtained her Ph.D. at New York University. Her postdoctoral work was in the Center for Neuroscience at University of California, Davis, and in the Department of Neurobiology at The University of Chicago.
Professor Lim’s research focuses on modeling and analysis of neuronal systems. Utilizing a broad spectrum of dynamical systems theory, the theory of stochastic processes, and information and control theories, she develop and analyze neural network models and synaptic plasticity rules for learning and memory. Her work accompanies analysis of neural data and a collaboration with experimentalists to provide and test biologically plausible models.
- Network modeling and analysis for short-term memory
- Modeling long-term synaptic plasticity for learning and long-term memory
- Analysis of variability or noise in neuronal systems
Education and Research Experience
- 1999-2003: B. S. in Mathematics and Physics, Seoul National University, Korea.
- 2004-2009: Ph. D. in Mathematics, New York University. Advisor: John Rinzel.
- 2009-2012: Postdoctoral scholar, University of California, Davis. Advisor: Mark S. Goldman.
- 2012-2015: Postdoctoral scholar, University of Chicago. Advisor: Nicolas Brunel.
- Lim S, McKee JL, Woloszyn L, Amit Y, Freedman DJ, Sheinberg DL, Brunel N, Inferring learning rules from distributions of firing rates in cortical neurons, Nature Neuroscience 18, 1804-1810 (2015)
- Lim S, Goldman MS, Balanced cortical microcircuitry for spatial working memory based on corrective feedback control, Journal of Neuroscience 34, 6790-6806 (2014)
- Lim S, Goldman MS, Balanced cortical microcircuitry for maintaining information in working memory, Nature Neuroscience 16, 1306-1314 (2013)
- S. Lim, M.S. Goldman, Noise tolerance of attractor and feedforward memory models, Neural Computation 24, 332-390 (2012).
- S. Lim, J. Rinzel, Noise-induced transitions in slow wave neuronal dynamics, Journal of Computational Neuroscience 28, 1-17 (2010).