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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Archer, E., Park, I.M. and Pillow, J. Bayesian entropy estimation for infinite neural alphabets 2012 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings URL
Archer, E., Park, I.M. and Pillow, J.W. Bayesian estimation of discrete entropy with mixtures of stick breaking priors 2012 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Archer, E., Park, I.M. and Pillow, J. Semi-parametric Bayesian entropy estimation for binary spike trains 2013 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Archer, E., Park, I.M. and Pillow, J. Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data 2013 Entropy
Vol. 15(5), pp. 1738-1755 
article DOI
Archer, E., Park, I.M. and Pillow, J.W. Bayesian entropy estimation for binary spike train data using parametric prior knowledge 2013 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Archer, E., Park, I.M. and Pillow, J. Bayesian Entropy Estimation for Countable Discrete Distributions 2014 Journal of Machine Learning Research
Vol. 15, pp. 2833-2868 
article URL arXiv
Archer, E., Park, I.M., Buesing, L., Cunningham, J. and Paninski, L. Black box variational inference for state space models 2015 ArXiv e-prints  unpublished arXiv
Arora, T. Exploring the expressive power of latent variable models 2023 School: Stony Brook University  mastersthesis
Arribas, D.M., Zhao, Y. and Park, I.M. Rescuing neural spike train models from bad MLE 2020 Advances in Neural Information Processing Systems (NeurIPS)  inproceedings URL arXiv code
Arribas, D., Zhao, Y. and Park, M. Framework to generate more realistic GLM spike trains 2021 Computational and Systems Neuroscience (COSYNE)  inproceedings
Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J.C. and Ache, B.W. Population coding within an ensemble of rhythmically active primary olfactory receptor 2009 Society for Neuroscience  inproceedings
Bobkov, Y., Ukhanov, K., Park, I., Príncipe, J.C. and Ache, B. Measuring Ensemble Activity in Lobster ORNs through Calcium Imaging 2010 Association for Chemoreception (AChemS) Annual Meeting  inproceedings
Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J.C. and Ache, B.W. Cellular basis for response diversity in the olfactory periphery 2012 PLoS One
Vol. 7(4), pp. e34843+ 
article DOI
Bobkov, Y., Park, I.M., Michaelis, B.T., Matthews, T., Reidenbach, M.A., Príncipe, J.C. and Ache, B. Rhythmically discharging olfactory receptor neurons can encode the spatiotemporal characteristics of odor signals within complexfluid environments 2018 European Chemoreception Research Organization (ECRO)  inproceedings URL
Bobkov, Y., Park, I., Michaelis, B.T., Matthews, T., Reidenbach, M.A., Príncipe, J.C. and Ache, B. Coding spatiotemporal characteristics of odor signals 2019 Association for Chemoreception (AChemS) Annual Meeting  inproceedings
Príncipe, J.C., Xu, J.W., Jenssen, R., Paiva, A. and Park, I. A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning 2010   inbook
Brinkman, B.A.W., Yan, H., Maffei, A., Park, I.M., Fontanini, A., Wang, J. and La Camera, G. Metastable dynamics of neural circuits and networks 2022 Applied Physics Reviews
Vol. 9(1), pp. 011313 
article DOI arXiv
Brockmeier, A.J., Park, I., Mahmoudi, B., Sanchez, J.C. and Príncipe, J.C. Spatio-Temporal Clustering of Firing Rates for Neural State Estimation 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)  inproceedings
Dikecligil, G.N., Graham, D., Park, I.M. and Fontanini, A. Layer Specific Sensorimotor Activity in the Gustatory Cortex of Licking Mice 2016 Society for Neuroscience  inproceedings
Dikecligil, G.N., Graham, D., Park, I.M. and Fontanini, A. Layer and cell type specific response properties of gustatory cortex neurons in awake mice 2020 Journal of Neuroscience  article DOI
Dockendorf, K., Park, I., He, P., Príncipe, J.C. and DeMarse, T.B. Liquid State Machines and Cultured Cortical Networks: The Separation Property 2009 Biosystems
Vol. 95(2), pp. 90-97 
article DOI
Dowling, M., Zhao, Y. and Park, I.M. Non-parametric generalized linear model 2020   unpublished arXiv
Dowling, M., Zhao, Y. and Park, M. NP-GLM: Nonparametric GLM 2021 Computational and Systems Neuroscience (COSYNE)  inproceedings
Dowling, M., Sokół, P. and Park, I.M. Hida-Matérn Kernel 2021   unpublished URL arXiv
Dowling, M., Sokół, P. and Park, I.M. Hida-Matérn Gaussian Processes 2022 Computational and Systems Neuroscience (COSYNE)  inproceedings
Dowling, M., Zhao, Y. and Park, I.M. Real-time variational method for learning neural trajectory and its dynamics 2023 International Conference on Learning Representations (ICLR)  inproceedings URL arXiv
Dowling, M., Zhao, Y. and Park, I.M. The Exponential Family Variational Kalman Filter for Real-time Neural Dynamics 2023 Computational and Systems Neuroscience (COSYNE)  inproceedings
Dowling, M., Zhao, Y. and Park, I.M. Linear time GPs for inferring latent trajectories from neural spike trains 2023 International Conference on Machine Learning (ICML)  inproceedings URL arXiv
Dowling, M., Zhao, Y. and Park, I.M. XFADS: Predicting single-trial cued behavior solely from preparatory activity 2024 Computational and Systems Neuroscience (COSYNE)  inproceedings
Esfahany, K., Siergiej, I., Zhao, Y. and Park, I.M. Organization of neural population code in mouse visual system 2018 Computational and Systems Neuroscience (COSYNE)  inproceedings
Esfahany, K., Siergiej, I., Zhao, Y. and Park, I.M. Organization of neural population code in mouse visual system 2018 eNeuro, pp. 0414-17  article DOI URL
Hocker, D. and Park, I.M. Multistep inference for generalized linear spiking models curbs runaway excitation 2017 8th International IEEE EMBS Conference On Neural Engineering, pp. 613-616  inproceedings DOI PDF
Hocker, D. and Park, I.M. Instability of the generalized linear model for spike trains 2017 Computational and Systems Neuroscience (COSYNE)  inproceedings
Hocker, D. and Park, I.M. Myopic Control: A New Control Objective for Neural Population Dynamics 2018 Computational and Systems Neuroscience (COSYNE)  inproceedings
Hocker, D. and Park, I.M. Myopic control of neural dynamics 2019 PLOS Computational Biology  article DOI URL
Huk, A., Yates, J., Katz, L., Park, I.M. and Pillow, J. Dissociated functional significance of choice-related activity across the primate dorsal stream 2014 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Jordan, I.D., Sokol, P.A. and Park, I.M. Gated recurrent units viewed through the lens of continuous time dynamical systems 2021 Frontiers in Computational Neuroscience  article DOI arXiv
Jordan, I.D. and Park, I.M. Birhythmic analog circuit maze: A nonlinear neurostimulation testbed 2020 Entropy
Vol. 22(5), pp. 537 
article DOI URL arXiv
Jordan, I., Sokol, P. and Park, I.M. Mechanisms Underlying Sequence-to-Sequence Working Memory 2021 DeepMath  conference URL
Jordan, I.D. Metastable Dynamics Underlying Neural Computation 2022 School: Stony Brook Univeersity  phdthesis
Kepple, D.R., Lee, D., Prepscius, C., Isler, V., Park, I.M. and Lee, D.L. Jointly learning visual motion and confidence from local patches in event cameras 2020 16th European conference on computer vision (ECCV2020)  inproceedings
spotlight
DOI
Kirschen, G.W., Ge, S. and Park, I.M. Probability of viral labeling of neural stem cells in vivo 2018 Neuroscience Letters  article DOI
Levi, A.J., Zhao, Y., Park, I.M. and Huk, A.C. Sensory and choice responses in MT distinct from motion encoding 2023 Journal of Neuroscience
Vol. 43(12), pp. 2090-2103 
article
(Levi and Zhao are co-first authors)
DOI URL
Li, L., Seth, S., Park, I., Sanchez, J.C. and Príncipe, J.C. Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)  inproceedings DOI
Li, L., Park, I., Seth, S., Sanchez, J.C. and Príncipe, J.C. Neuronal Functional Connectivity Dynamics in Cortex: An MSC-based Analysis 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)  inproceedings
Li, L., Park, I., Seth, S., Sanchez, J.C. and Príncipe, J.C. Functional Connectivity Dynamics Among Cortical Neurons: A Dependence Analysis 2012 IEEE Transactions on Neural Systems and Rehabilitation Engineering
Vol. 20(1), pp. 18-30 
article DOI
Li, L., Park, I.M., Seth, S., Choi, J., Francis, J.T., Sanchez, J.C. and Príncipe, J.C. An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-square (KLMS) algorithm 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  conference DOI
Li, L., Park, I.M., Brockmeier, A., Chen, B., Seth, S., Francis, J.T., Sanchez, J.C. and Príncipe, J.C. Adaptive Inverse Control of Neural Spatiotemporal Spike Patterns with a Reproducing Kernel Hilbert Space (RKHS) Framework 2012 IEEE Transactions on Neural Systems and Rehabilitation Engineering
Vol. 21(4), pp. 532-543 
article DOI
Liu, W., Park, I., Wang, Y. and Príncipe, J.C. Extended Kernel Recursive Least Squares Algorithm 2009 IEEE Transactions on Signal Processing
Vol. 57(10), pp. 3801-3814 
article DOI
Liu, W., Park, I. and Príncipe, J.C. An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters 2009 IEEE Transactions on Neural Network
Vol. 20(12), pp. 1950-1961 
article DOI
Michaelis, B., Leathers, K., Ache, B., Bobkov, Y., Principe, J., Baharloo, R., Park, I.M. and Reidenbach, M. Odor tracking in marine organisms: the importance of temporal and spatial intermittency of the odor signal 2020 Scientific Reports
Vol. 10, pp. 7961 
article DOI
Mofakham, S., Fry, A., Adachi, J., Ashcroft, B., Winans, N., Liang, J., Sharma, H., Fiore, S., Park, I.M. and Mikell, C. Recovery of consciousness after traumatic brain injury: Biomarkers and a mechanistic model 2019 Computational and Systems Neuroscience (COSYNE)  inproceedings
Mofakham, S., Fry, A., Adachi, J., Winans, N., Liang, J., Ashcroft, B., Sharma, H., Fiore, S., Park, I.M. and Mikell, C. Electrophysiological prognostication of functional cortical integrity after traumatic brain injury 2019 American Association of Neurological Surgeons (AANS)  inproceedings
Nassar, J., Linderman, S., Zhao, Y., Bugallo, M. and Park, I.M. Learning structured neural dynamics from single trial population recording 2018 52nd Asilomar Conference on Signals, Systems and Computers  inproceedings URL PDF
Nassar, J., Linderman, S.W., Bugallo, M. and Park, I.M. Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling 2019 International Conference on Learning Representations (ICLR)  inproceedings URL arXiv code
Nassar, J., Linderman, S., Park, I.M. and Bugallo, M. Tree-structured locally linear dynamics model to uproot Bayesian neural data analysis 2019 Computational and Systems Neuroscience (COSYNE)  inproceedings
Nassar, J., Sokol, P., Chung, S., Harris, K. and Park, I.M. Spectral regularization in biological and artificial neural networks 2020 Computational and Systems Neuroscience (COSYNE)  inproceedings
Nassar, J., Sokol, P., Chang, S., Harris, K. and Park, I.M. On 1/n neural representation and robustness 2020 Advances in Neural Information Processing Systems (NeurIPS)  inproceedings
(JN and PS are co-first authors)
URL arXiv
Nassar, J., Sokol, P., Chung, S.Y., Harris, K. and Park, I.M. On 1/n neural representation and robustness 2020 DeepMath  conference URL
Nassar, J. Bayesian Machine Learning for Analyzing and Controlling Neural Populations 2022 School: Stony Brook University  phdthesis
Neophytou, D., Arribas, D., Oviedo, H. and Park, I.M. Quasi-Bayesian estimation of time constants supports lateralized auditory computation 2021 Computational and Systems Neuroscience (COSYNE)  inproceedings
Neophytou, D., Arribas, D., Levy, R., Arora, T., Park, I.M. and Oviedo, H.V. Differences in temporal processing speeds between the right and left auditory cortex reflect the strength of recurrent synaptic connectivity 2022 PLoS Biology
Vol. 20(10), pp. e3001803 
article DOI URL
Paiva, A.R.C., Rao, S., Park, I. and Príncipe, J.C. Spectral Clustering of Synchronous Spike Trains 2007 IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1831-1835  inproceedings DOI URL
Paiva, A.R.C., Park, I. and Príncipe, J.C. Innovating Signal Processing for Spike Train Data 2007 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  inproceedings
Paiva, A.R.C., Park, I. and Príncipe, J.C. Reproducing Kernel Hilbert Spaces for Spike Train Analysis 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)  inproceedings DOI
Paiva, A.R.C., Park, I. and Príncipe, J.C. A Reproducing Kernel Hilbert Space framework for Spike Trains 2009 Neural Computation
Vol. 21(2), pp. 424-449 
article DOI
Paiva, A.R.C., Park, I., Sanchez, J. and Príncipe, J.C. Peri-event Cross-Correlation over Time for Analysis of Interactions in Neuronal Firing 2008 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  inproceedings
Paiva, A.R.C., Park, I. and Príncipe, J.C. A Comparison of Binless Spike Train Measures 2010 Neural Computing & Applications
Vol. 19, pp. 405-419 
article DOI
Paiva, A.R.C., Park, I. and Príncipe, J.C. Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains ? Computational Neuroscience  inbook
(in press)
Paiva, A.R.C., Park, I. and Príncipe, J.C. Inner Products for Representation and Learning in the Spike Train Domain 2010 Statistical Signal Processing for Neuroscience  inbook
Paiva, A.R.C. and Park, I. Which measure should we use for unsupervised spike train learning? 2010 Statistical Analysis of Neuronal Data (SAND5)  conference
Paiva, A.R.C., Park, I., Príncipe, J.C. and Sanchez, J. Instantaneous cross-correlation analysis of neural ensembles with high temporal resolution 2013 Neural engineering applied to neurorehabilitation  inbook
Park, I. and Park, J.C. Modeling Causality in Biological Pathways for Logical Identification of Drug Targets 2005 Bioinfo  inproceedings
Park, I., Xu, D., DeMarse, T.B. and Príncipe, J.C. Modeling of Synchronized Burst in Dissociated Cortical Tissue: An Exploration of Parameter Space 2006 IEEE International Joint Conference on Neural Networks (IJCNN)  inproceedings DOI
Park, I., Paiva, A.R.C., DeMarse, T.B., Príncipe, J.C. and Harris, J. A Closed Form Solution for Multiple-Input Spike Based Adaptive Filters 2007 IEEE International Joint Conference on Neural Networks (IJCNN)  inproceedings
Park, I., Paiva, A.R.C., Príncipe, J.C. and DeMarse, T.B. An Efficient Computation of Continuous-time Correlogram of Spike Trains 2007 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I., Paiva, A.R.C., DeMarse, T.B. and Príncipe, J.C. An efficient algorithm for continuous-time cross correlogram of spike trains 2008 Journal of Neuroscience Methods
Vol. 168(2), pp. 514-523 
article DOI
Park, I. and Príncipe, J.C. Correntropy based Granger causality 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)  inproceedings DOI
Park, I., Rao, M., DeMarse, T.B. and Príncipe, J.C. Point Process Model for Precisely Timed Spike Trains. 2009 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings DOI
Park, I., Bobkov, Y., Ukhanov, K., Ache, B.W. and Príncipe, J.C. Input Driven Synchrony of Oscillating Olfactory Receptor Neurons: A Computational Modeling Study 2009 Association for Chemoreception (AChemS) Annual Meeting  inproceedings
Park, I. and Príncipe, J.C. Significance test for spike trains based on finite point process estimation 2009 Society for Neuroscience  inproceedings
Park, I.M. Capturing spike train similarity structure: A point process divergence approach 2010 School: The University of Florida  phdthesis
Park, I. and Príncipe, J.C. Quantification of Inter-trial Non-stationarity in Spike Trains from Periodically Stimulated Neural Cultures 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5442-5445  inproceedings
Special session on Multivariate Analysis of Brain Signals: Methods and Applications
DOI
Park, I.M., Meister, M., Huk, A. and Pillow, J.W. Detailed encoding and decoding of choice-related information from LIP spike trains 2011 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I.M., Seth, S. and Príncipe, J.C. Spike Train Kernel Methods for Neuroscience 2011 Joint Statistical Meeting  conference
Park, I.M. and Pillow, J.W. Bayesian Spike Triggered Covariance Analysis 2011 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Park, I., Seth, S., Rao, M. and Principe, J.C. Estimation of symmetric chi-square divergence for point processes 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2016-2019  inproceedings DOI
Park, I.M., Seth, S., Rao, M. and Príncipe, J.C. Strictly positive definite spike train kernels for point process divergences 2012 Neural Computation
Vol. 24(8), pp. 2223-2250 
article DOI PDF
Park, I.M. and Pillow, J. Bayesian spike-triggered covariance and the elliptical LNP model 2012 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I.M., Nassar, M. and Park, M. Active Bayesian Optimization: Minimizing Minimizer Entropy 2012 ArXiv e-prints  unpublished arXiv code
Park, I.M., Meister, M.L.R., Huk, A.C. and Pillow, J.W. Deciphering the code for sensorimotor decision-making at the level of single neurons in parietal cortex 2012 Society for Neuroscience  inproceedings
(oral presentation)
Park, I.M., Seth, S., Paiva, A.R.C., Li, L. and Principe, J.C. Kernel methods on spike train space for neuroscience: a tutorial 2013 IEEE Signal Processing Magazine
Vol. 30(4), pp. 149-160 
article DOI arXiv
Park, I.M., Archer, E., Priebe, N. and Pillow, J. Got a moment or two? Neural models and linear dimensionality reduction 2013 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I.M., Archer, E. and Pillow, J. Bayesian entropy estimators for spike trains 2013 Computational Neuroscience (CNS)  inproceedings
Park, I.M., Archer, E., Latimer, K. and Pillow, J.W. Universal models for binary spike patterns using centered Dirichlet processes 2013 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Park, I.M., Archer, E., Priebe, N. and Pillow, J.W. Spectral methods for neural characterization using generalized quadratic models 2013 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Park, I.M., Seth, S. and Van Vaerenbergh, S. Bayesian Extensions of Kernel Least Mean Squares 2013 ArXiv e-prints  unpublished arXiv
Park, I.M., Bobkov, Y.V., Ache, B.W. and Príncipe, J.C. Intermittency coding in the primary olfactory system: A neural substrate for olfactory scene analysis 2014 The Journal of Neuroscience
Vol. 34(3), pp. 941-952 
article DOI
Park, I.M., Archer, E., Latimer, K. and Pillow, J. Scalable nonparametric models for binary spike patterns 2014 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I.M., Seth, S. and Van Vaerenbergh, S. Probabilistic Kernel Least Mean Squares Algorithms 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)  inproceedings DOI URL
Park, I.M., Meister, M.L.R., Huk, A.C. and Pillow, J.W. Encoding and decoding in parietal cortex during sensorimotor decision-making 2014 Nature Neuroscience
Vol. 17(10), pp. 1395-1403 
article DOI PDF
Park, I.M., Yates, J., Huk, A. and Pillow, J. Dynamic correlations between visual and decision areas during perceptual decision-making 2015 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I.M. and Pillow, J.W. Bayesian Efficient Coding 2020 bioRxiv, pp. 178418+  unpublished
(under review)
DOI URL data
Park, I.M., Ságodi, Á. and Sokół, P.A. Persistent learning signals and working memory without continuous attractors 2023   unpublished arXiv
Park, I.M., Ságodi, Á. and Sokół, P.A. Persistent learning signals without continuous attractors 2023 Bernstein Conference  inproceedings DOI URL
Park, I.M. Persistent activity bump on a ring without a continuous ring attractor 2024 Computational and Systems Neuroscience (COSYNE)  inproceedings
Park, I. Continuous time correlation analysis techniques for spike trains 2007 School: The University of Florida  mastersthesis
Pei, F., Ye, J., Sedler, A.R., Zoltowski, D., Wu, A., Chowdhury, R.H., Sohn, H., O'Doherty, J.E., Shenoy, K.V., Kaufman, M.T., Churchland, M., Jazayeri, M., Miller, L.E., Park, I.M., Dyer, E., Pillow, J. and Pandarinath, C. Advancing the investigation of neural population structure with the Neural Latents Benchmark 2021 Society for Neuroscience  inproceedings
Pei, F., Ye, J., Zoltowski, D., Wu, A., Chowdhury, R.H., Sohn, H., O'Doherty, J.E., Shenoy, K.V., Kaufman, M.T., Churchland, M., Jazayeri, M., Miller, L.E., Pillow, J., Park, I.M., Dyer, E.L. and Pandarinath, C. Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity 2021 Advances in Neural Information Processing Systems (NeurIPS)  inproceedings
(PF and YU are co-first authors)
URL arXiv
Pillow, J. and Park, I.M. Beyond Barlow: A Bayesian theory of efficient neural coding 2013 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Príncipe, J.C. Information Theoretic Learning 2010   book URL
Seth, S., Park, I. and Príncipe, J.C. A new nonparametric measure of conditional independence 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)  inproceedings DOI
Seth, S., Park, I., Brockmeier, A.J., Semework, M., Choi, J., Francis, J. and Príncipe, J.C. A novel family of non-parametric cumulative based divergences for point processes 2010 Advances in Neural Information Processing Systems (NIPS), pp. 2119-2127  inproceedings URL
Seth, S., Rao, M., Park, I. and Príncipe, J.C. A Unified Framework for Quadratic Measures of Independence 2011 IEEE Transactions on Signal Processing
Vol. 59, pp. 3624-3635 
article DOI
Sokol, P. and Park, I.M. Information geometry of orthogonal initializations and training 2020 International Conference on Learning Representations (ICLR)  inproceedings URL arXiv
Sokol, P., Jordan, I., Kadile, E. and Park, I.M. Adjoint dynamics of stable limit cycle neural networks 2019 53rd Asilomar Conference on Signals, Systems and Computers  inproceedings DOI PDF
Sokol, P. and Park, I.M. Limit Cycle Neural Networks Have Infinite Memory 2019 DeepMath  conference URL
Sokol, P., Jordan, I. and Park, I.M. Information geometry at initialization and beyond 2019 DeepMath  conference URL
Sokół, P. and Park, I.M. Only two types of attractors support representation of continuous variables, and learning over long time-spans 2023 Computational and Systems Neuroscience (COSYNE)  inproceedings
Sokół, P. Geometry of learning and representation in neural networks 2023 School: Stony Brook University  phdthesis
Stone, I., Sagiv, Y., Park, I.M. and Pillow, J.W. Spectral learning of Bernoulli latent dynamical system models for decision-making 2023 Computational and Systems Neuroscience (COSYNE)  inproceedings
Stone, I.R., Sagiv, Y., Park, I.M. and Pillow, J.W. Spectral learning of Bernoulli linear dynamical systems models for decision-making 2023 Transactions on Machine Learning Research  article URL arXiv code
Stone, I., Sagiv, Y., Park, I.M. and Pillow, J. Spectral learning of Bernoulli linear dynamical systems models 2023 Bernstein Conference  inproceedings DOI URL
Vermani, A., Chen, K., Kogan, J., Fontanini, A. and Park, I.M. Can time dependent and invariant decoders co-exist? 2022 Computational and Systems Neuroscience (COSYNE)  inproceedings
Vermani, A., Nassar, J. and Park, I.M. Aligning high-dimensional neural recordings for data efficient inference of dynamics and prediction 2023 Bernstein Conference  inproceedings DOI URL
Vermani, A., Park, I.M. and Nassar, J. Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data 2024 International Conference on Learning Representations (ICLR)  inproceedings URL
Wu, A., Park, I.M. and Pillow, J. Convolutional spike-triggered covariance analysis for estimating subunit models 2015 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Wu, A., Park, I.M. and Pillow, J. Convolutional spike-triggered covariance analysis for neural subunit models 2015 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL
Xu, J.W., Paiva, A.R.C., Park, I. and Príncipe, J.C. A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning 2008 IEEE Transactions on Signal Processing
Vol. 56(12), pp. 5891-5902 
article DOI
Yates, J., Park, I.M., Cormack, L., Pillow, J. and Huk, A. Precise characterization of multiple LIP neurons in relation to stimulus and behavior 2013 Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE)  inproceedings
Yates, J., Park, I.M., Cormack, L., Pillow, J. and Huk, A. Precise characterization of dorsal stream neural activity during decision making 2013 Society for Neuroscience  inproceedings
Yates, J.L., Katz, L.N., Park, I.M., Pillow, J.W. and Huk, A.C. Correlations and choice probabilities in simultaneously recorded MT and LIP neurons 2014 Society for Neuroscience  inproceedings
Yates, J., Archer, E., Huk, A.C. and Park, I.M. Canonical correlations reveal co-variability between spike trains and local field potentials in area MT 2015 Organization for Computational Neuroscience (CNS)  inproceedings
Yates, J.L., Park, I.M., Katz, L.N., Pillow, J.W. and Huk, A.C. Functional dissection of signal and noise in MT and LIP during decision-making 2017 Nature Neuroscience
Vol. 20, pp. 1285-1292 
article DOI
Zhao, Y. and Park, I.M. Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains 2017 Neural Computation
Vol. 29(5) 
article DOI youtube arXiv code
Zhao, Y. and Park, I.M. Inferring low-dimensional network dynamics with variational latent Gaussian process 2016 Organization for Computational Neuroscience (CNS)  inproceedings
Zhao, Y. and Park, I.M. Variational inference of latent Gaussian neural dynamics 2016 International Conference on Machine Learning (ICML) Workshop on Computational Biology  inproceedings
Zhao, Y. and Park, I.M. Interpretable Nonlinear Dynamic Modeling of Neural Trajectories 2016 Advances in Neural Information Processing Systems (NIPS)  inproceedings URL youtube arXiv
Zhao, Y. Log-linear Model Based Tree and Latent Variable Model for Count Data 2016 School: Stony Brook University  phdthesis
Zhao, Y. and Park, I.M. Gotta infer'em all: dynamical features from neural trajectories 2017 Computational and Systems Neuroscience (COSYNE)  inproceedings
Zhao, Y., Yates, J. and Park, I.M. Low-dimensional state-space trajectory of choice at the population level in area MT 2017 Computational and Systems Neuroscience (COSYNE)  inproceedings
Zhao, Y. and Park, I.M. Variational online learning of neural dynamics 2020 Frontiers in Computational Neuroscience  article DOI arXiv code
Zhao, Y. and Park, I.M. Accessing neural states in real time: recursive variational Bayesian dual estimation 2018 Computational and Systems Neuroscience (COSYNE)  inproceedings
Zhao, Y., Nassar, J., Jordan, I., Bugallo, M. and Park, I.M. Streaming Variational Monte Carlo 2022 IEEE Transactions on Pattern Analysis and Machine Intelligence
Vol. 45(1), pp. 1150-1161 
article DOI URL arXiv code
Zhao, Y., Yates, J.L., Levi, A., Huk, A. and Park, I.M. Stimulus-choice (mis)alignment in primate area MT 2020 PLOS Computational Biology  article DOI URL data youtube
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