| Author | Title | Year | Journal/Proceedings | Reftype | DOI/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L., Cunningham, J. and Paninski, L. | Black box variational inference for state space models | 2015 | ArXiv e-prints | unpublished | arXiv |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
(top 25%) |
URL youtube 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 | |
| Dowling, M., Zhao, Y. and Park, I.M. | eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling | 2024 | Advances in Neural Information Processing Systems (NeurIPS) | inproceedings | URL arXiv |
| 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 |
| Filipe, A.C. and Park, I.M. | NeuroTask: A Benchmark Dataset for Multi-Task Neural Analysis | 2024 | Bernstein Conference | inproceedings | DOI URL code |
| 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 | |
| Jeon, H., Dowling, M. and Park, I.M. | Closed-loop active sensing for nonlinear system identification in attractor dynamics disorders | 2024 | European Conference on Brain Stimulation | conference
(oral presentation) |
|
| Jeon, H. and Park, I.M. | Quantifying signal-to-noise ratio in neural latent trajectories via Fisher information | 2024 | European Signal Processing Conference | inproceedings | arXiv |
| 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 | |
| Ságodi, Á., Martín-Sánchez, G., Sokół, P. and Park, I.M. | Back to the Continuous Attractor | 2024 | Advances in Neural Information Processing Systems (NeurIPS) | inproceedings | URL arXiv |
| Ságodi, Á., Martín-Sánchez, G., Sokół, P. and Park, I.M. | Slow Manifold Dynamics for Working Memory are near Continuous Attractors | 2024 | Bernstein Conference | inproceedings | DOI 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 |
| Vermani, A., Dowling, M., Jeon, H., Jordan, I., Nassar, J., Bernaerts, Y., Zhao, Y., Vaerenbergh, S.V. and Park, I.M. | Real-time machine learning strategies for a new kind of neuroscience experiments | 2024 | European Signal Processing Conference | inproceedings | arXiv |
| Vermani, A., Nassar, J., Jeon, H., Dowling, M. and Park, I.M. | Meta-dynamical state space models for integrative neural data analysis | 2024 | arXiv [stat.ML] | unpublished
(under review) |
URL arXiv |
| 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 |
| Zhao, Y., Nassar, J. and Park, I.M. | Real-time discovery of effective dynamics from streaming noisy neural observations | 2020 | Computational and Systems Neuroscience (COSYNE) | inproceedings |
CATNIP Lab Publication
Computational and Theoretical Neural Information Processing Laboratory