Il Memming Park, Ph.D.

Affiliations: 
Center for Perceptual Systems Stony Brook University, Stony Brook, NY, United States 
Area:
Computational Neuroscience, spike train, point process, computational methods, decision, olfaction
Website:
http://catniplab.github.io
Google:
"Memming"
Bio:

https://scholar.google.com/citations?user=CsmltusAAAAJ

Mean distance: 15.99 (cluster 51)
 
SNBCP

Children

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Josue Nassar grad student SUNY Stony Brook
Logan Becker grad student 2017- SUNY Stony Brook
Piotr Sokol grad student 2017- SUNY Stony Brook
Ian Jordan grad student 2018- SUNY Stony Brook
Yuan Zhao grad student 2015-2016 SUNY Stony Brook
David Lance Hocker post-doc 2016- SUNY Stony Brook (Chemistry Tree)
Sima Mofakham post-doc 2018- SUNY Stony Brook

Collaborators

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Yuriy Bobkov collaborator 2008- University of Florida
Alex C. Huk collaborator 2010- UT Austin
Jacob Yates collaborator 2011- UT Austin
Alfredo Fontanini collaborator 2016- SUNY Stony Brook
Charles Mikell III collaborator 2016- SUNY Stony Brook
Thomas DeMarse collaborator 2005-2009 University of Florida
Murali Rao collaborator 2007-2010 University of Florida (MathTree)
BETA: Related publications

Publications

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Park IM, Meister ML, Huk AC, et al. (2014) Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience. 17: 1395-403
Park IM, Bobkov YV, Ache BW, et al. (2014) Intermittency coding in the primary olfactory system: a neural substrate for olfactory scene analysis. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 34: 941-52
Park IM, Seth S, Van Vaerenbergh S. (2014) Probabilistic kernel least mean squares algorithms Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 8272-8276
Archer E, Park IM, Pillow JW. (2014) Bayesian entropy estimation for countable discrete distributions Journal of Machine Learning Research. 15: 2833-2868
Li L, Park IM, Brockmeier A, et al. (2013) Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society. 21: 532-43
Archer E, Park IM, Pillow JW. (2013) Bayesian and quasi-Bayesian estimators for mutual information from discrete data Entropy. 15: 1738-1755
Park IM, Seth S, Paiva ARC, et al. (2013) Kernel methods on spike train space for neuroscience: A tutorial Ieee Signal Processing Magazine. 30: 149-160
Park IM, Archer E, Priebe N, et al. (2013) Spectral methods for neural characterization using generalized quadratic models Advances in Neural Information Processing Systems
Park IM, Archer E, Latimer K, et al. (2013) Universal models for binary spike patterns using centered Dirichlet processes Advances in Neural Information Processing Systems
Archer E, Park IM, Pillow JW. (2013) Bayesian entropy estimation for binary spike train data using parametric prior knowledge Advances in Neural Information Processing Systems
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