Publications

Ságodi, Á. and Park, I. M. (2026). Dynamical archetype analysis: Autonomous computation. Computational and Systems Neuroscience (COSYNE).

Ságodi, Á. and Park, I. M. (2026). Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees. . http://arxiv.org/abs/2602.08640

Kautz, J., Vermani, A. and Park, I. M. (2026). Tracking Changes in Neural Dynamics over the Course of Learning. Computational and Systems Neuroscience (COSYNE).

Vermani, A. (2025). Identifying Invariant Representations underlying Neural Computation. . http://hdl.handle.net/10362/190610

Vermani, A., Nassar, J., Jeon, H., Dowling, M. and Park, I. M. (2025). Meta-dynamical state space models for integrative neural data analysis. Computational and Systems Neuroscience (COSYNE).

Vermani, A., Nassar, J., Jeon, H., Dowling, M. and Park, I. M. (2025). Meta-dynamical state space models for integrative neural data analysis. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=SRpq5OBpED

Ságodi, Á. and Park, I. M. (2025). Dynamical archetype analysis: Autonomous computation. . http://arxiv.org/abs/2507.05505

Ságodi, Á., Martín-Sánchez, G., Sokół, P. and Park, I. M. (2025). Approximate continuous attractor theory. Computational and Systems Neuroscience (COSYNE). https://www.world-wide.org/cosyne-25/approximate-continuous-attractor-25422ef2/

Nassar, J., Zhao, Y., Jordan, I. and Park, I. M. (2025). System and method of model-based machine learning for non-episodic state space exploration. United States Patent and Trademark Office(20250209344:A1). https://patentimages.storage.googleapis.com/27/07/f1/3ce01937875384/US20250209344A1.pdf

Liang, A., Ságodi, Á. and Sokó, P. (2025). Symmetry-Regularized Learning of Continuous Attractor Dynamics. NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations (NeurReps 2025). https://openreview.net/forum?id=W8Gf7CYCo8

Filipe, C., Elmakki, M., Costa-Ferreira, G. and Park, I. M. (2025). Conditional diffusion framework for analyzing neural dynamics across multiple contexts. Computational and Systems Neuroscience (COSYNE).

Vermani, A., Dowling, M., Jeon, H., Jordan, I., Nassar, J., Bernaerts, Y., Zhao, Y., Vaerenbergh, S. V. and Park, I. M. (2024). Real-time machine learning strategies for a new kind of neuroscience experiments. European Signal Processing Conference.

Vermani, A., Park, I. M. and Nassar, J. (2024). Leveraging generative models for unsupervised alignment of neural time series data. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=9zhHVyLY4K

Ságodi, Á., Martín-Sánchez, G., Sokół, P. and Park, I. M. (2024). Slow manifold dynamics for working memory are near continuous attractors. Bernstein Conference. https://doi.org/https://doi.org/10.12751/nncn.bc2024.076 https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/5964bd61-91e8-4d1a-a9ca-e6d137f456b4

Ságodi, Á., Martín-Sánchez, G., Sokół, P. and Park, I. M. (2024). Back to the continuous attractor. Advances in Neural Information Processing Systems (NeurIPS). https://doi.org/https://doi.org/10.52202/079017-2136 https://openreview.net/forum?id=fvG6ZHrH0B

Park, I. M. (2024). Persistent activity bump on a ring without a continuous ring attractor. Computational and Systems Neuroscience (COSYNE).

Jeon, H. and Park, I. M. (2024). Quantifying signal-to-noise ratio in neural latent trajectories via Fisher information. European Signal Processing Conference.

Jeon, H., Dowling, M. and Park, I. M. (2024). Closed-loop active sensing for nonlinear system identification in attractor dynamics disorders. European Conference on Brain Stimulation.

Filipe, A. C. and Park, I. M. (2024). NeuroTask: A benchmark dataset for multi-task neural analysis. Bernstein Conference. https://doi.org/https://doi.org/10.12751/nncn.bc2024.020 https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/ccb5dd62-403b-4da0-a839-2652ab70ac28

Dowling, M., Zhao, Y. and Park, I. M. (2024). eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling. Advances in Neural Information Processing Systems (NeurIPS). https://doi.org/https://doi.org/10.52202/079017-0430 https://openreview.net/forum?id=Ln8ogihZ2S

Dowling, M., Zhao, Y. and Park, I. M. (2024). XFADS: Predicting single-trial cued behavior solely from preparatory activity. Computational and Systems Neuroscience (COSYNE).

Vermani, A., Nassar, J. and Park, I. M. (2023). Aligning high-dimensional neural recordings for data efficient inference of dynamics and prediction. Bernstein Conference. https://doi.org/https://doi.org/10.12751/nncn.bc2023.291 https://abstracts.g-node.org/conference/BC23/abstracts#/uuid/9c78d90e-d9e2-4ac6-9c41-e00328c57426

Stone, I., Sagiv, Y., Park, I. M. and Pillow, J. (2023). Spectral learning of Bernoulli linear dynamical systems models. Bernstein Conference. https://doi.org/https://doi.org/10.12751/nncn.bc2023.288 https://abstracts.g-node.org/conference/BC23/abstracts#/uuid/9067fd8a-f2c4-4535-a686-8fe99c653492

Stone, I. R., Sagiv, Y., Park, I. M. and Pillow, J. W. (2023). Spectral learning of Bernoulli linear dynamical systems models for decision-making. Transactions on Machine Learning Research. https://openreview.net/forum?id=giw2vcAhiH

Stone, I., Sagiv, Y., Park, I. M. and Pillow, J. W. (2023). Spectral learning of Bernoulli latent dynamical system models for decision-making. Computational and Systems Neuroscience (COSYNE).

Sokół, P. (2023). Geometry of learning and representation in neural networks. .

Sokół, P. and Park, I. M. (2023). Only two types of attractors support representation of continuous variables, and learning over long time-spans. Computational and Systems Neuroscience (COSYNE).

Park, I. M., Ságodi, Á. and Sokół, P. A. (2023). Persistent learning signals without continuous attractors. Bernstein Conference. https://doi.org/https://doi.org/10.12751/nncn.bc2023.348 https://abstracts.g-node.org/conference/BC23/abstracts#/uuid/ecbf6440-d119-4cbc-8324-665e7cefe8d6

Park, I. M., Ságodi, Á. and Sokół, P. A. (2023). Persistent learning signals and working memory without continuous attractors. .

Levi, A. J., Zhao, Y., Park, I. M. and Huk, A. C. (2023). Sensory and choice responses in MT distinct from motion encoding. Journal of Neuroscience, 43(12), 2090-2103. https://doi.org/https://doi.org/10.1523/JNEUROSCI.0267-22.2023 https://www.jneurosci.org/content/43/12/2090

Dowling, M., Zhao, Y. and Park, I. M. (2023). Linear time GPs for inferring latent trajectories from neural spike trains. International Conference on Machine Learning (ICML). https://openreview.net/forum?id=1hWB5XEUMa

Dowling, M., Zhao, Y. and Park, I. M. (2023). The Exponential Family Variational Kalman Filter for Real-time Neural Dynamics. Computational and Systems Neuroscience (COSYNE).

Dowling, M., Zhao, Y. and Park, I. M. (2023). Real-time variational method for learning neural trajectory and its dynamics. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=M_MvkWgQSt

Arora, T. (2023). Exploring the expressive power of latent variable models. .

Zhao, Y., Nassar, J., Jordan, I., Bugallo, M. and Park, I. M. (2022). Streaming variational Monte Carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 1150-1161. https://doi.org/https://doi.org/10.1109/TPAMI.2022.3153225 http://dx.doi.org/10.1109/TPAMI.2022.3153225

Vermani, A., Chen, K., Kogan, J., Fontanini, A. and Park, I. M. (2022). Can time dependent and invariant decoders co-exist?. Computational and Systems Neuroscience (COSYNE).

Neophytou, D., Arribas, D., Levy, R., Arora, T., Park, I. M. and Oviedo, H. V. (2022). Differences in temporal processing speeds between the right and left auditory cortex reflect the strength of recurrent synaptic connectivity. PLoS Biology, 20(10), e3001803. https://doi.org/https://doi.org/10.1371/journal.pbio.3001803 https://www.biorxiv.org/content/10.1101/2021.04.14.439872

Nassar, J. (2022). Bayesian Machine Learning for Analyzing and Controlling Neural Populations. .

Jordan, I. D. (2022). Metastable Dynamics Underlying Neural Computation. .

Dowling, M., Sokół, P. and Park, I. M. (2022). Hida-Matérn Gaussian Processes. Computational and Systems Neuroscience (COSYNE).

Brinkman, B. A. W., Yan, H., Maffei, A., Park, I. M., Fontanini, A., Wang, J. and La Camera, G. (2022). Metastable dynamics of neural circuits and networks. Applied Physics Reviews, 9(1), 011313. https://doi.org/https://doi.org/10.1063/5.0062603

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. (2021). Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. Advances in Neural Information Processing Systems (NeurIPS). https://openreview.net/forum?id=KVMS3fl4Rsv

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. (2021). Advancing the investigation of neural population structure with the Neural Latents Benchmark. Society for Neuroscience.

Neophytou, D., Arribas, D., Oviedo, H. and Park, I. M. (2021). Quasi-Bayesian estimation of time constants supports lateralized auditory computation. Computational and Systems Neuroscience (COSYNE).

Jordan, I., Sokol, P. and Park, I. M. (2021). Mechanisms Underlying Sequence-to-Sequence Working Memory. DeepMath. https://www.deepmath-conference.com

Jordan, I. D., Sokol, P. A. and Park, I. M. (2021). Gated recurrent units viewed through the lens of continuous time dynamical systems. Frontiers in Computational Neuroscience. https://doi.org/https://doi.org/10.3389/fncom.2021.678158

Dowling, M., Sokół, P. and Park, I. M. (2021). Hida-Matérn Kernel. . http://arxiv.org/abs/2107.07098

Dowling, M., Zhao, Y. and Park, M. (2021). NP-GLM: Nonparametric GLM. Computational and Systems Neuroscience (COSYNE).

Arribas, D., Zhao, Y. and Park, M. (2021). Framework to generate more realistic GLM spike trains. Computational and Systems Neuroscience (COSYNE).

Zhao, Y., Nassar, J. and Park, I. M. (2020). Real-time discovery of effective dynamics from streaming noisy neural observations. Computational and Systems Neuroscience (COSYNE).

Zhao, Y., Yates, J. L., Levi, A., Huk, A. and Park, I. M. (2020). Stimulus-choice (mis)alignment in primate area MT. PLOS Computational Biology. https://doi.org/https://doi.org/10.1371/journal.pcbi.1007614 https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007614

Zhao, Y. and Park, I. M. (2020). Variational online learning of neural dynamics. Frontiers in Computational Neuroscience. https://doi.org/https://doi.org/10.3389/fncom.2020.00071

Sokol, P. A., Jordan, I., Kadile, E. and Park, I. M. (2020). Unexpected benefits of learning with neural oscillation: stable backpropagation with limit cycles. From Neuroscience to Artificially Intelligent Systems (NAISys).

Sokol, P. and Park, I. M. (2020). Information geometry of orthogonal initializations and training. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=rkg1ngrFPr

Park, I. M. and Pillow, J. W. (2020). Bayesian Efficient Coding. bioRxiv, 178418+. https://doi.org/https://doi.org/10.1101/178418 https://www.biorxiv.org/content/10.1101/178418v3

Nassar, J., Sokol, P., Chung, S. Y., Harris, K. and Park, I. M. (2020). On 1/n neural representation and robustness. DeepMath. https://www.deepmath-conference.com

Nassar, J., Sokol, P., Chang, S., Harris, K. and Park, I. M. (2020). On 1/n neural representation and robustness. Advances in Neural Information Processing Systems (NeurIPS). https://papers.nips.cc/paper/2020/hash/44bf89b63173d40fb39f9842e308b3f9-Abstract.html

Nassar, J., Sokol, P., Chung, S., Harris, K. and Park, I. M. (2020). Spectral regularization in biological and artificial neural networks. Computational and Systems Neuroscience (COSYNE).

Michaelis, B., Leathers, K., Ache, B., Bobkov, Y., Principe, J., Baharloo, R., Park, I. M. and Reidenbach, M. (2020). Odor tracking in marine organisms: the importance of temporal and spatial intermittency of the odor signal. Scientific Reports, 10, 7961. https://doi.org/https://doi.org/10.1038/s41598-020-64766-y

Kepple, D. R., Lee, D., Prepscius, C., Isler, V., Park, I. M. and Lee, D. L. (2020). Jointly learning visual motion and confidence from local patches in event cameras. 16th European conference on computer vision (ECCV2020). https://doi.org/https://doi.org/10.1007/978-3-030-58539-6_30

Jordan, I. D. and Park, I. M. (2020). Birhythmic analog circuit maze: A nonlinear neurostimulation testbed. Entropy, 22(5), 537. https://doi.org/https://doi.org/10.3390/e22050537 https://www.mdpi.com/1099-4300/22/5/537

Dowling, M., Zhao, Y. and Park, I. M. (2020). Non-parametric generalized linear model. .

Dikecligil, G. N., Graham, D., Park, I. M. and Fontanini, A. (2020). Layer and cell type specific response properties of gustatory cortex neurons in awake mice. Journal of Neuroscience. https://doi.org/https://doi.org/10.1523/JNEUROSCI.1579-19.2020

Arribas, D. M., Zhao, Y. and Park, I. M. (2020). Rescuing neural spike train models from bad MLE. Advances in Neural Information Processing Systems (NeurIPS). https://papers.nips.cc/paper/2020/hash/186b690e29892f137b4c34cfa40a3a4d-Abstract.html

Sokol, P., Jordan, I. and Park, I. M. (2019). Information geometry at initialization and beyond. DeepMath. https://www.deepmath-conference.com

Sokol, P. and Park, I. M. (2019). Limit Cycle Neural Networks Have Infinite Memory. DeepMath. https://www.deepmath-conference.com

Sokol, P., Jordan, I., Kadile, E. and Park, I. M. (2019). Adjoint dynamics of stable limit cycle neural networks. 53rd Asilomar Conference on Signals, Systems and Computers. https://doi.org/https://doi.org/10.1109/IEEECONF44664.2019.9049080

Nassar, J., Linderman, S., Park, I. M. and Bugallo, M. (2019). Tree-structured locally linear dynamics model to uproot Bayesian neural data analysis. Computational and Systems Neuroscience (COSYNE).

Nassar, J., Linderman, S. W., Bugallo, M. and Park, I. M. (2019). Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=HkzRQhR9YX

Mofakham, S., Fry, A., Adachi, J., Winans, N., Liang, J., Ashcroft, B., Sharma, H., Fiore, S., Park, I. M. and Mikell, C. (2019). Electrophysiological prognostication of functional cortical integrity after traumatic brain injury. American Association of Neurological Surgeons (AANS).

Mofakham, S., Fry, A., Adachi, J., Ashcroft, B., Winans, N., Liang, J., Sharma, H., Fiore, S., Park, I. M. and Mikell, C. (2019). Recovery of consciousness after traumatic brain injury: Biomarkers and a mechanistic model. Computational and Systems Neuroscience (COSYNE).

Hocker, D. and Park, I. M. (2019). Myopic control of neural dynamics. PLOS Computational Biology. https://doi.org/https://doi.org/10.1371/journal.pcbi.1006854 https://www.biorxiv.org/content/early/2017/12/30/241299

Bobkov, Y., Park, I., Michaelis, B. T., Matthews, T., Reidenbach, M. A., Príncipe, J. C. and Ache, B. (2019). Coding spatiotemporal characteristics of odor signals. Association for Chemoreception (AChemS) Annual Meeting.

Zhao, Y. and Park, I. M. (2018). Accessing neural states in real time: recursive variational Bayesian dual estimation. Computational and Systems Neuroscience (COSYNE).

Nassar, J., Linderman, S., Zhao, Y., Bugallo, M. and Park, I. M. (2018). Learning structured neural dynamics from single trial population recording. 52nd Asilomar Conference on Signals, Systems and Computers. https://ieeexplore.ieee.org/document/8645122

Kirschen, G. W., Ge, S. and Park, I. M. (2018). Probability of viral labeling of neural stem cells in vivo. Neuroscience Letters. https://doi.org/https://doi.org/10.1016/j.neulet.2018.05.016

Hocker, D. and Park, I. M. (2018). Myopic Control: A New Control Objective for Neural Population Dynamics. Computational and Systems Neuroscience (COSYNE).

Esfahany, K., Siergiej, I., Zhao, Y. and Park, I. M. (2018). Organization of neural population code in mouse visual system. eNeuro, 0414-17. https://doi.org/https://doi.org/10.1523/ENEURO.0414-17.2018 https://www.biorxiv.org/content/early/2018/03/25/220558

Esfahany, K., Siergiej, I., Zhao, Y. and Park, I. M. (2018). Organization of neural population code in mouse visual system. Computational and Systems Neuroscience (COSYNE).

Bobkov, Y., Park, I. M., Michaelis, B. T., Matthews, T., Reidenbach, M. A., Príncipe, J. C. and Ache, B. (2018). Rhythmically discharging olfactory receptor neurons can encode the spatiotemporal characteristics of odor signals within complexfluid environments. European Chemoreception Research Organization (ECRO). https://coms.events/ECRO2018/data/abstracts/en/abstract_0089.html

Zhao, Y., Yates, J. and Park, I. M. (2017). Low-dimensional state-space trajectory of choice at the population level in area MT. Computational and Systems Neuroscience (COSYNE).

Zhao, Y. and Park, I. M. (2017). Gotta infer'em all: dynamical features from neural trajectories. Computational and Systems Neuroscience (COSYNE).

Zhao, Y. and Park, I. M. (2017). Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains. Neural Computation, 29(5). https://doi.org/https://doi.org/10.1162/NECO_a_00953

Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. and Huk, A. C. (2017). Functional dissection of signal and noise in MT and LIP during decision-making. Nature Neuroscience, 20, 1285-1292. https://doi.org/https://doi.org/10.1038/nn.4611

Hocker, D. and Park, I. M. (2017). Instability of the generalized linear model for spike trains. Computational and Systems Neuroscience (COSYNE).

Hocker, D. and Park, I. M. (2017). Multistep inference for generalized linear spiking models curbs runaway excitation. 8th International IEEE EMBS Conference On Neural Engineering, 613-616. https://doi.org/https://doi.org/10.1109/ner.2017.8008426

Zhao, Y. (2016). Log-linear Model Based Tree and Latent Variable Model for Count Data. .

Zhao, Y. and Park, I. M. (2016). Interpretable Nonlinear Dynamic Modeling of Neural Trajectories. Advances in Neural Information Processing Systems (NIPS). https://papers.nips.cc/paper/6543-interpretable-nonlinear-dynamic-modeling-of-neural-trajectories

Zhao, Y. and Park, I. M. (2016). Variational inference of latent Gaussian neural dynamics. International Conference on Machine Learning (ICML) Workshop on Computational
Biology.

Zhao, Y. and Park, I. M. (2016). Inferring low-dimensional network dynamics with variational latent
Gaussian process. Organization for Computational Neuroscience (CNS).

Dikecligil, G. N., Graham, D., Park, I. M. and Fontanini, A. (2016). Layer Specific Sensorimotor Activity in the Gustatory Cortex of Licking
Mice. Society for Neuroscience.

Yates, J., Archer, E., Huk, A. C. and Park, I. M. (2015). Canonical correlations reveal co-variability between spike trains
and local field potentials in area MT. Organization for Computational Neuroscience (CNS).

Wu, A., Park, I. M. and Pillow, J. (2015). Convolutional spike-triggered covariance analysis for neural subunit models. Advances in Neural Information Processing Systems (NIPS). https://papers.nips.cc/paper/5962-convolutional-spike-triggered-covariance-analysis-for-neural-subunit-models

Wu, A., Park, I. M. and Pillow, J. (2015). Convolutional spike-triggered covariance analysis for estimating subunit models. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Yates, J., Huk, A. and Pillow, J. (2015). Dynamic correlations between visual and decision areas during perceptual decision-making. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Archer, E., Park, I. M., Buesing, L., Cunningham, J. and Paninski, L. (2015). Black box variational inference for state space models. ArXiv e-prints.

Yates, J. L., Katz, L. N., Park, I. M., Pillow, J. W. and Huk, A. C. (2014). Correlations and choice probabilities in simultaneously recorded
MT and LIP neurons. Society for Neuroscience.

Park, I. M., Meister, M. L. R., Huk, A. C. and Pillow, J. W. (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience, 17(10), 1395-1403. https://doi.org/https://doi.org/10.1038/nn.3800

Park, I. M., Seth, S. and Van Vaerenbergh, S. (2014). Probabilistic Kernel Least Mean Squares Algorithms. IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP). https://doi.org/https://doi.org/10.1109/ICASSP.2014.6855214 http://gtas.unican.es/pub/393

Park, I. M., Archer, E., Latimer, K. and Pillow, J. (2014). Scalable nonparametric models for binary spike patterns. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Bobkov, Y. V., Ache, B. W. and Príncipe, J. C. (2014). Intermittency coding in the primary olfactory system: A neural substrate
for olfactory scene analysis. The Journal of Neuroscience, 34(3), 941-952. https://doi.org/https://doi.org/10.1523/jneurosci.2204-13.2014

Huk, A., Yates, J., Katz, L., Park, I. M. and Pillow, J. (2014). Dissociated functional significance of choice-related activity across the primate dorsal stream. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Archer, E., Park, I. M. and Pillow, J. (2014). Bayesian Entropy Estimation for Countable Discrete Distributions. Journal of Machine Learning Research, 15, 2833-2868. http://jmlr.org/papers/v15/archer14a.html

Yates, J., Park, I. M., Cormack, L., Pillow, J. and Huk, A. (2013). Precise characterization of dorsal stream neural activity during
decision making. Society for Neuroscience.

Yates, J., Park, I. M., Cormack, L., Pillow, J. and Huk, A. (2013). Precise characterization of multiple LIP neurons in relation to stimulus and behavior. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Pillow, J. and Park, I. M. (2013). Beyond Barlow: A Bayesian theory of efficient neural coding. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Seth, S. and Van Vaerenbergh, S. (2013). Bayesian Extensions of Kernel Least Mean Squares. ArXiv e-prints.

Park, I. M., Archer, E., Priebe, N. and Pillow, J. W. (2013). Spectral methods for neural characterization using generalized quadratic models. Advances in Neural Information Processing Systems (NIPS). http://papers.nips.cc/paper/4993-spectral-methods-for-neural-characterization-using-generalized-quadratic-models

Park, I. M., Archer, E., Latimer, K. and Pillow, J. W. (2013). Universal models for binary spike patterns using centered Dirichlet processes. Advances in Neural Information Processing Systems (NIPS). http://papers.nips.cc/paper/5050-universal-models-for-binary-spike-patterns-using-centered-dirichlet-processes

Park, I. M., Archer, E. and Pillow, J. (2013). Bayesian entropy estimators for spike trains. Computational Neuroscience (CNS).

Park, I. M., Archer, E., Priebe, N. and Pillow, J. (2013). Got a moment or two? Neural models and linear dimensionality reduction. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Seth, S., Paiva, A. R. C., Li, L. and Principe, J. C. (2013). Kernel methods on spike train space for neuroscience: a tutorial. IEEE Signal Processing Magazine, 30(4), 149-160. https://doi.org/https://doi.org/10.1109/msp.2013.2251072

Paiva, A. R. C., Park, I., Príncipe, J. C. and Sanchez, J. (2013). Instantaneous cross-correlation analysis of neural ensembles with high temporal resolution. Neural engineering applied to neurorehabilitation.

Archer, E., Park, I. M. and Pillow, J. W. (2013). Bayesian entropy estimation for binary spike train data using parametric prior knowledge. Advances in Neural Information Processing Systems (NIPS). http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge

Archer, E., Park, I. M. and Pillow, J. (2013). Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. Entropy, 15(5), 1738-1755. https://doi.org/https://doi.org/10.3390/e15051738

Archer, E., Park, I. M. and Pillow, J. (2013). Semi-parametric Bayesian entropy estimation for binary spike trains. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Meister, M. L. R., Huk, A. C. and Pillow, J. W. (2012). Deciphering the code for sensorimotor decision-making at the level of single neurons in parietal cortex. Society for Neuroscience.

Park, I. M., Nassar, M. and Park, M. (2012). Active Bayesian Optimization: Minimizing Minimizer Entropy. ArXiv e-prints.

Park, I. M. and Pillow, J. (2012). Bayesian spike-triggered covariance and the elliptical LNP model. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Park, I. M., Seth, S., Rao, M. and Príncipe, J. C. (2012). Strictly positive definite spike train kernels for point process divergences. Neural Computation, 24(8), 2223-2250. https://doi.org/https://doi.org/10.1162/NECO_a_00309

Li, L., Park, I. M., Brockmeier, A., Chen, B., Seth, S., Francis, J. T., Sanchez, J. C. and Príncipe, J. C. (2012). Adaptive Inverse Control of Neural Spatiotemporal Spike Patterns with a Reproducing Kernel Hilbert Space (RKHS) Framework. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(4), 532-543. https://doi.org/https://doi.org/10.1109/TNSRE.2012.2200300

Li, L., Park, I., Seth, S., Sanchez, J. C. and Príncipe, J. C. (2012). Functional Connectivity Dynamics Among Cortical Neurons: A Dependence Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(1), 18-30. https://doi.org/https://doi.org/10.1109/TNSRE.2011.2176749

Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J. C. and Ache, B. W. (2012). Cellular basis for response diversity in the olfactory periphery. PLoS One, 7(4), e34843+. https://doi.org/https://doi.org/10.1371/journal.pone.0034843

Archer, E., Park, I. M. and Pillow, J. W. (2012). Bayesian estimation of discrete entropy with mixtures of stick breaking priors. Advances in Neural Information Processing Systems (NIPS). https://papers.nips.cc/paper/4521-bayesian-estimation-of-discrete-entropy-with-mixtures-of-stick-breaking-priors

Archer, E., Park, I. M. and Pillow, J. (2012). Bayesian entropy estimation for infinite neural alphabets. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE). http://papers.nips.cc/paper/4521-bayesian-estimation-of-discrete-entropy-with-mixtures-of-stick-breaking-priors

Seth, S., Rao, M., Park, I. and Príncipe, J. C. (2011). A Unified Framework for Quadratic Measures of Independence. IEEE Transactions on Signal Processing, 59, 3624-3635. https://doi.org/https://doi.org/10.1109/TSP.2011.2153197

Park, I., Seth, S., Rao, M. and Principe, J. C. (2011). Estimation of symmetric chi-square divergence for point processes. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016-2019. https://doi.org/https://doi.org/10.1109/ICASSP.2011.5946907

Park, I. M. and Pillow, J. W. (2011). Bayesian Spike Triggered Covariance Analysis. Advances in Neural Information Processing Systems (NIPS). http://papers.nips.cc/paper/4411-bayesian-spike-triggered-covariance-analysis

Park, I. M., Seth, S. and Príncipe, J. C. (2011). Spike Train Kernel Methods for Neuroscience. Joint Statistical Meeting.

Park, I. M., Meister, M., Huk, A. and Pillow, J. W. (2011). Detailed encoding and decoding of choice-related information from LIP spike trains. Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE).

Li, L., Park, I. M., Seth, S., Choi, J., Francis, J. T., Sanchez, J. C. and Príncipe, J. C. (2011). An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-square (KLMS) algorithm. IEEE International Workshop on Machine Learning for Signal Processing (MLSP). https://doi.org/https://doi.org/10.1109/MLSP.2011.6064603

Seth, S., Park, I., Brockmeier, A. J., Semework, M., Choi, J., Francis, J. and Príncipe, J. C. (2010). A novel family of non-parametric cumulative based divergences for point processes. Advances in Neural Information Processing Systems (NIPS), 2119-2127. http://papers.nips.cc/paper/4126-a-novel-family-of-non-parametric-cumulative-based-divergences-for-point-processes

Príncipe, J. C. (2010). Information Theoretic Learning. . http://www.springer.com/us/book/9781441915696

Park, I. and Príncipe, J. C. (2010). Quantification of Inter-trial Non-stationarity in Spike Trains from Periodically Stimulated Neural Cultures. IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 5442-5445. https://doi.org/https://doi.org/10.1109/ICASSP.2010.5494920

Park, I. M. (2010). Capturing spike train similarity structure: A point process divergence approach. .

Paiva, A. R. C. and Park, I. (2010). Which measure should we use for unsupervised spike train learning?. Statistical Analysis of Neuronal Data (SAND5).

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2010). Inner Products for Representation and Learning in the Spike Train Domain. Statistical Signal Processing for Neuroscience.

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2010). A Comparison of Binless Spike Train Measures. Neural Computing & Applications, 19, 405-419. https://doi.org/https://doi.org/10.1007/s00521-009-0307-6

Li, L., Park, I., Seth, S., Sanchez, J. C. and Príncipe, J. C. (2010). Neuronal Functional Connectivity Dynamics in Cortex: An MSC-based Analysis. Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBS).

Brockmeier, A. J., Park, I., Mahmoudi, B., Sanchez, J. C. and Príncipe, J. C. (2010). Spatio-Temporal Clustering of Firing Rates for Neural State Estimation. Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBS).

Príncipe, J. C., Xu, J. W., Jenssen, R., Paiva, A. and Park, I. (2010). A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning. .

Bobkov, Y., Ukhanov, K., Park, I., Príncipe, J. C. and Ache, B. (2010). Measuring Ensemble Activity in Lobster ORNs through Calcium Imaging. Association for Chemoreception (AChemS) Annual Meeting.

Seth, S., Park, I. and Príncipe, J. C. (2009). A new nonparametric measure of conditional independence. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). https://doi.org/https://doi.org/10.1109/ICASSP.2009.4960250

Park, I. and Príncipe, J. C. (2009). Significance test for spike trains based on finite point process estimation. Society for Neuroscience.

Park, I., Bobkov, Y., Ukhanov, K., Ache, B. W. and Príncipe, J. C. (2009). Input Driven Synchrony of Oscillating Olfactory Receptor Neurons: A Computational Modeling Study. Association for Chemoreception (AChemS) Annual Meeting.

Park, I., Rao, M., DeMarse, T. B. and Príncipe, J. C. (2009). Point Process Model for Precisely Timed Spike Trains.. Frontiers in Systems Neuroscience. Conference Abstract: Computational
and Systems Neuroscience (COSYNE). https://doi.org/https://doi.org/10.3389/conf.neuro.06.2009.03.227

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2009). A Reproducing Kernel Hilbert Space framework for Spike Trains. Neural Computation, 21(2), 424-449. https://doi.org/https://doi.org/10.1162/neco.2008.09-07-614

Liu, W., Park, I. and Príncipe, J. C. (2009). An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters. IEEE Transactions on Neural Network, 20(12), 1950-1961. https://doi.org/https://doi.org/10.1109/TNN.2009.2033676

Liu, W., Park, I., Wang, Y. and Príncipe, J. C. (2009). Extended Kernel Recursive Least Squares Algorithm. IEEE Transactions on Signal Processing, 57(10), 3801-3814. https://doi.org/https://doi.org/10.1109/TSP.2009.2022007

Li, L., Seth, S., Park, I., Sanchez, J. C. and Príncipe, J. C. (2009). Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks. Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBS). https://doi.org/https://doi.org/10.1109/IEMBS.2009.5333991

Dockendorf, K., Park, I., He, P., Príncipe, J. C. and DeMarse, T. B. (2009). Liquid State Machines and Cultured Cortical Networks: The Separation Property. Biosystems, 95(2), 90-97. https://doi.org/https://doi.org/10.1016/j.biosystems.2008.08.001

Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J. C. and Ache, B. W. (2009). Population coding within an ensemble of rhythmically active primary olfactory receptor. Society for Neuroscience.

Xu, J. W., Paiva, A. R. C., Park, I. and Príncipe, J. C. (2008). A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning. IEEE Transactions on Signal Processing, 56(12), 5891-5902. https://doi.org/https://doi.org/10.1109/TSP.2008.2005085

Park, I. and Príncipe, J. C. (2008). Correntropy based Granger causality. IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP). https://doi.org/https://doi.org/10.1109/ICASSP.2008.4518432

Park, I., Paiva, A. R. C., DeMarse, T. B. and Príncipe, J. C. (2008). An efficient algorithm for continuous-time cross correlogram of spike trains. Journal of Neuroscience Methods, 168(2), 514-523. https://doi.org/https://doi.org/10.1016/j.jneumeth.2007.10.005

Paiva, A. R. C., Park, I., Sanchez, J. and Príncipe, J. C. (2008). Peri-event Cross-Correlation over Time for Analysis of Interactions in Neuronal Firing. International Conference of the IEEE Engineering in Medicine and
Biology Society (EMBC).

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2008). Reproducing Kernel Hilbert Spaces for Spike Train Analysis. IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP). https://doi.org/https://doi.org/10.1109/ICASSP.2008.4518834

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2008). Reproducing Kernel Hilbert Spaces for Spike Train Analysis. Conference on Computational Neuroscience.

Park, I. (2007). Continuous time correlation analysis techniques for spike trains. .

Park, I., Paiva, A. R. C., Príncipe, J. C. and DeMarse, T. B. (2007). An Efficient Computation of Continuous-time Correlogram of Spike Trains. Frontiers in Systems Neuroscience. Conference Abstract: Computational
and Systems Neuroscience (COSYNE).

Park, I., Paiva, A. R. C., DeMarse, T. B., Príncipe, J. C. and Harris, J. (2007). A Closed Form Solution for Multiple-Input Spike Based Adaptive Filters. IEEE International Joint Conference on Neural Networks (IJCNN).

Paiva, A. R. C., Park, I. and Príncipe, J. C. (2007). Innovating Signal Processing for Spike Train Data. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Paiva, A. R. C., Rao, S., Park, I. and Príncipe, J. C. (2007). Spectral Clustering of Synchronous Spike Trains. IEEE International Joint Conference on Neural Networks (IJCNN), 1831-1835. https://doi.org/https://doi.org/10.1109/IJCNN.2007.4371236 http://dx.doi.org/10.1109/IJCNN.2007.4371236

Park, I., Xu, D., DeMarse, T. B. and Príncipe, J. C. (2006). Modeling of Synchronized Burst in Dissociated Cortical Tissue: An Exploration of Parameter Space. IEEE International Joint Conference on Neural Networks (IJCNN). https://doi.org/https://doi.org/10.1109/IJCNN.2006.246734

Park, I. and Park, J. C. (2005). Modeling Causality in Biological Pathways for Logical Identification of Drug Targets. Bioinfo.

Paiva, A. R. C., Park, I. and Príncipe, J. C. (). Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains. .

Paiva, A. R. C., Park, I. and Príncipe, J. C. (?). Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains. Computational Neuroscience.