Il Memming Park
Il Memming Park
Associate Professor of Neurobiology and Behavior, Stony Brook University
Verified email at - Homepage
Cited by
Cited by
An information theoretic approach of designing sparse kernel adaptive filters
W Liu, I Park, JC Príncipe
Neural Networks, IEEE Transactions on 20 (12), 1950-1961, 2009
Encoding and decoding in parietal cortex during sensorimotor decision-making
IM Park, MLR Meister, AC Huk, JW Pillow
Nature neuroscience 17 (10), 1395-1403, 2014
Extended kernel recursive least squares algorithm
W Liu, I Park, Y Wang, JC Príncipe
Signal Processing, IEEE Transactions on 57 (10), 3801-3814, 2009
Black box variational inference for state space models
E Archer, IM Park, L Buesing, J Cunningham, L Paninski
arXiv preprint arXiv:1511.07367, 2015
A reproducing kernel hilbert space framework for spike train signal processing
ARC Paiva, I Park, JC Príncipe
Neural computation 21 (2), 424-449, 2009
Variational latent gaussian process for recovering single-trial dynamics from population spike trains
Y Zhao, IM Park
Neural computation 29 (5), 1293-1316, 2017
Spectral methods for neural characterization using generalized quadratic models
IM Park, EW Archer, N Priebe, JW Pillow
Advances in neural information processing systems 26, 2454-2462, 2013
A comparison of binless spike train measures
ARC Paiva, I Park, JC Príncipe
Neural computing & applications 19 (3), 405-419, 2010
Bayesian Spike-Triggered Covariance Analysis
IM Park, JW Pillow
Advances in neural information processing systems (NIPS), 2011
Functional dissection of signal and noise in MT and LIP during decision-making
JL Yates, IM Park, LN Katz, JW Pillow, AC Huk
Nature neuroscience 20 (9), 1285-1292, 2017
Bayesian entropy estimation for countable discrete distributions
E Archer, IM Park, JW Pillow
The Journal of Machine Learning Research 15 (1), 2833-2868, 2014
A reproducing kernel Hilbert space framework for information-theoretic learning
JW Xu, ARC Paiva, I Park, JC Principe
Signal Processing, IEEE Transactions on 56 (12), 5891-5902, 2008
Liquid state machines and cultured cortical networks: The separation property
KP Dockendorf, I Park, P He, JC Príncipe, TB DeMarse
Biosystems 95 (2), 90-97, 2009
Bayesian efficient coding
IM Park, JW Pillow
BioRxiv, 178418, 2020
Kernel methods on spike train space for neuroscience: a tutorial
IM Park, S Seth, ARC Paiva, L Li, JC Principe
IEEE Signal Processing Magazine 30 (4), 149-160, 2013
Tree-structured recurrent switching linear dynamical systems for multi-scale modeling
J Nassar, S Linderman, M Bugallo, IM Park
International Conference on Learning Representation (ICLR) 2019, 2018
Bayesian and quasi-Bayesian estimators for mutual information from discrete data
E Archer, IM Park, JW Pillow
Entropy 15 (5), 1738-1755, 2013
Interpretable nonlinear dynamic modeling of neural trajectories
Y Zhao, IM Park
arXiv preprint arXiv:1608.06546, 2016
Strictly positive-definite spike train kernels for point-process divergences
IM Park, S Seth, M Rao, JC Principe
Neural Computation 24 (8), 2223-2250, 2012
Intermittency coding in the primary olfactory system: a neural substrate for olfactory scene analysis
IM Park, YV Bobkov, BW Ache, JC Príncipe
Journal of Neuroscience 34 (3), 941-952, 2014
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