Il Memming Park
Il Memming Park
Associate Professor of Neurobiology and Behavior, Stony Brook University
Підтверджена електронна адреса в stonybrook.edu - Домашня сторінка
Назва
Посилання
Посилання
Рік
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
2152009
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
1922014
Extended kernel recursive least squares algorithm
W Liu, I Park, Y Wang, JC Príncipe
Signal Processing, IEEE Transactions on 57 (10), 3801-3814, 2009
1882009
Black box variational inference for state space models
E Archer, IM Park, L Buesing, J Cunningham, L Paninski
arXiv preprint arXiv:1511.07367, 2015
1142015
A reproducing kernel hilbert space framework for spike train signal processing
ARC Paiva, I Park, JC Príncipe
Neural computation 21 (2), 424-449, 2009
822009
A comparison of binless spike train measures
ARC Paiva, I Park, JC Príncipe
Neural computing & applications 19 (3), 405-419, 2010
792010
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
74*2013
Bayesian Spike-Triggered Covariance Analysis
IM Park, JW Pillow
Advances in neural information processing systems (NIPS), 2011
742011
Variational latent gaussian process for recovering single-trial dynamics from population spike trains
Y Zhao, IM Park
Neural computation 29 (5), 1293-1316, 2017
722017
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, 2017
692017
Bayesian entropy estimation for countable discrete distributions
E Archer, IM Park, JW Pillow
The Journal of Machine Learning Research 15 (1), 2833-2868, 2014
642014
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
642008
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
532009
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
422013
Bayesian efficient coding
IM Park, JW Pillow
BioRxiv, 178418, 2017
412017
Interpretable nonlinear dynamic modeling of neural trajectories
Y Zhao, IM Park
arXiv preprint arXiv:1608.06546, 2016
262016
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
262014
Bayesian and quasi-Bayesian estimators for mutual information from discrete data
E Archer, IM Park, JW Pillow
Entropy 15 (5), 1738-1755, 2013
262013
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
252018
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
EW Archer, IM Park, JW Pillow
Advances in neural information processing systems, 1700-1708, 2013
252013
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