Research Interests
计算神经科学，学习与记忆，网络相互作用，动力系统
Research Summary
Sukbin Lim教授利用广泛的动力系统理论、随机过程理论以及信息和控制理论，开发并分析了用于学习与记忆的神经网络模型和突触可塑性规则。她还通过对神经数据的分析和与实验人员的合作来提供和测试生物学上可行的模型。
Education Background
2009年 纽约大学数学博士
2003年 首尔大学数学和物理学学士
Work/Research Experience
2012年－2015年 芝加哥大学博士后研究员
2009年－2012年 加利福尼亚大学戴维斯分校博士后研究员
Representative Publications
- S. Lim (2019) Mechanism underlying sharpening of visual response dynamics with familiarity, eLife, 8: e44098.
- S.J. Sylvester, M.M. Lee, A.D. Ramirez, S. Lim, M.S. Goldman, E.R.F. Aksay (2017) Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior, Scientific Reports, 7, 16240.
- S. Lim, J.L. Mckee, D.J. Freedman, Y. Amit, D.L. Sheinberg, N. Brunel (2015) Inferring learning rules from distributions of firing rates in cortical neurons, Nature Neuroscience, 18: 1804-1810.
- S. Lim [co-corresponding author], M.S. Goldman (2014) Balanced cortical microcircuitry for spatial working memory based on corrective feedback control, Journal of Neuroscience, 34: 6790-6806.
- S. Lim, M.S. Goldman (2013) Balanced cortical microcircuitry for maintaining information in working memory, Nature Neuroscience, 16: 1306-1314.
- S. Lim, M.S. Goldman (2012) Noise tolerance of attractor and feedforward memory models, Neural Computation, 24: 332-390.
- S. Lim [corresponding author], J. Rinzel (2010) Noise-induced transitions in slow wave neuronal dynamics, Journal Computational Neuroscience, 28: 1-17.