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Memming Park

Faculty Profile Park
Associate Professor
PhD, University of Florida

memming.park@stonybrook.edu

Life Sciences Building
Office: Room 550B
Phone: (631) 632-3468 - office
Fax: (631) 632-6661

http://catniplab.github.io
 


Training

I. Memming Park (박일) received a B.S. in computer science from KAIST in 2005. He received an M.S. in electrical and computer engineering in 2007 and a Ph.D. in biomedical engineering in 2010 from the University of Florida working with José C. Principe. He was a postdoctoral fellow (2010-2014) at the University of Texas at Austin working with Jonathan Pillow, before he joined the faculty of neurobiology and behavior at Stony Brook University in 2015.

Research Interests/Expertise

I am interested in how information is encoded as spatiotemporal patterns of neural activity, and how information is processed to perform specific computations within and across brain areas. For instance, I analyze how visual motion is represented by visual cortices, and how it is subsequently integrated over time to form decisions that are represented in higher order cortices. I collaborate with experimental neurophysiologists who record neural activities, and I model their data to find structures hidden in noisy observations; structures within the signals themselves, and to relate neural activity with external stimulus and behavior.

  • Publications

    Diego M. Arribas, Yuan Zhao, and Il Memming Park. Rescuing neural spike train models from bad MLE. In Advances in Neural Information Processing Systems (NeurIPS), 2020

    Josue Nassar, Piotr Sokol, SueYeon Chang, Kenneth Harris, and Il Memming Park. On 1/n neural representation and robustness. In Advances in Neural Information Processing Systems (NeurIPS), 2020

    Yuan Zhao and Il Memming Park. Variational online learning of neural dynamics. Frontiers in Computational Neuroscience, 2020

    Yuan Zhao, Jacob L. Yates, Aaron Levi, Alex Huk, and Il Memming Park. Stimulus-choice (mis)alignment in primate area MT. PLOS Computational Biology, 2020

    Piotr Sokol and Il Memming Park. Information geometry of orthogonal initializations and training. In International Conference on Learning Representations (ICLR), 2020

    Josue Nassar, Scott W. Linderman, Monica Bugallo, and Il Memming Park. Tree-structured recurrent switching linear dynamical systems for multi-scale modeling. In International Conference on Learning Representations (ICLR), 2019

    David Hocker and Il Memming Park. Myopic control of neural dynamics. PLOS Computational Biology, 2019

    Kathleen Esfahany, Isabel Siergiej, Yuan Zhao, and Il Memming Park. Organization of neural population code in mouse visual system. eNeuro, pages 0414–17, 2018

    Yuan Zhao and Il Memming Park. Variational latent Gaussian process for recovering single-trial dynamics from population spike trains. Neural Computation, 29(5), 2017

    Yuan Zhao and Il Memming Park. Interpretable nonlinear dynamic modeling of neural trajectories. In Advances in Neural Information Processing Systems (NIPS), 2016

    Il Memming Park, Miriam L. R. Meister, Alexander C. Huk, and Jonathan W. Pillow. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience, 17(10):1395–1403, 2014

    Evan Archer, Il Memming Park, and Jonathan Pillow. Bayesian entropy estimation for countable discrete distributions. Journal of Machine Learning Research, 15:2833–2868, 2014

    Il Memming Park, Yuriy V. Bobkov, Barry W. Ache, and Jose C. Principe. Intermittency coding in the primary olfactory system: A neural substrate for olfactory scene analysis. The Journal of Neuroscience, 34(3):941–952, 2014

    Il Memming Park, Sohan Seth, Antonio R. C. Paiva, Lin Li, and Jose C. Principe. Kernel methods on spike train space for neuroscience: a tutorial. IEEE Signal Processing Magazine, 30(4):149–160, 2013

    up to date info
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  • Laboratory Personnel

    Yuan Zhao, Postdoc

    Piotr Sokol, PhD student

    Josue Nassar, PhD student

    Ian Jordan, PhD student

    Ayesha Vermani, PhD student

    Matthew Dowling, PhD student

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