Modeling of Synchronized Burst in Dissociated Cortical Tissue: An Exploration of Parameter Space. - Il Park 002; Dongming Xu; Thomas B. DeMarse; José C. Príncipe (IJCNN, 2006)

Spectral Clustering of Synchronous Spike Trains. - António R. C. Paiva; Sudhir Rao; Il Park 002; José C. Príncipe (IJCNN, 2007)

A Closed Form Solution for Multiple-Input Spike Based Adaptive Filters. - Il Park 002; António R. C. Paiva; José C. Príncipe; John G. Harris (IJCNN, 2007)

Reproducing kernel Hilbert spaces for spike train analysis. - António R. C. Paiva; Il Park 002; José C. Príncipe (ICASSP, 2008)

Correntropy based Granger causality. - Il Park 002; José Carlos Príncipe (ICASSP, 2008)

A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning. - Jian-Wu Xu; António R. C. Paiva; Il Park 002; José Carlos Príncipe (IEEE Trans. Signal Process., 2008)

A new nonparametric measure of conditional independence. - Sohan Seth; Il Park 002; José C. Príncipe (ICASSP, 2009)

Liquid state machines and cultured cortical networks: The separation property. - Karl P. Dockendorf; Il Park 002; Ping He; José C. Príncipe; Thomas B. DeMarse (Biosyst., 2009)

A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing. - António R. C. Paiva; Il Park 002; José C. Príncipe (Neural Comput., 2009)

An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters. - Weifeng Liu 001; Il Park 002; José C. Príncipe (IEEE Trans. Neural Networks, 2009)

Extended kernel recursive least squares algorithm. - Weifeng Liu 001; Il Park 002; Yiwen Wang 002; José C. Príncipe (IEEE Trans. Signal Process., 2009)

A Reproducing Kernel Hilbert Space Framework for ITL. - Jian-Wu Xu; Robert Jenssen; António R. C. Paiva; Il Park 002 (Information Theoretic Learning, 2010)

Quantification of inter-trial non-stationarity in spike trains from periodically stimulated neural cultures. - Il Park 002; José C. Príncipe (ICASSP, 2010)

A novel family of non-parametric cumulative based divergences for point processes. - Sohan Seth; Il Memming Park; Austin J. Brockmeier; Mulugeta Semework; John S. Choi; Joseph T. Francis; José Carlos Príncipe (NIPS, 2010)

A comparison of binless spike train measures. - António R. C. Paiva; Il Park 002; José C. Príncipe (Neural Comput. Appl., 2010)

Estimation of symmetric chi-square divergence for point processes. - Il Park 002; Sohan Seth; Murali Rao; José Carlos Príncipe (ICASSP, 2011)

An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS). - Lin Li 020; Il Memming Park; Sohan Seth; John S. Choi; Joseph T. Francis; Justin C. Sanchez; José C. Príncipe (MLSP, 2011)

Bayesian Spike-Triggered Covariance Analysis. - Il Memming Park; Jonathan W. Pillow (NIPS, 2011)

A Unified Framework for Quadratic Measures of Independence. - Sohan Seth; Murali Rao; Il Park 002; José C. Príncipe (IEEE Trans. Signal Process., 2011)

Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. - Evan Archer; Il Memming Park; Jonathan W. Pillow (NIPS, 2012)

Active Bayesian Optimization: Minimizing Minimizer Entropy - Il Memming Park; Marcel Nassar; Mijung Park (CoRR, 2012)

Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences. - Il Memming Park; Sohan Seth; Murali Rao; José C. Príncipe (Neural Comput., 2012)

Bayesian entropy estimation for binary spike train data using parametric prior knowledge. - Evan Archer; Il Memming Park; Jonathan W. Pillow (NIPS, 2013)

Universal models for binary spike patterns using centered Dirichlet processes. - Il Memming Park; Evan Archer; Kenneth W. Latimer; Jonathan W. Pillow (NIPS, 2013)

Spectral methods for neural characterization using generalized quadratic models. - Il Memming Park; Evan Archer; Nicholas Priebe; Jonathan W. Pillow (NIPS, 2013)

Bayesian Extensions of Kernel Least Mean Squares. - Il Memming Park; Sohan Seth; Steven Van Vaerenbergh (CoRR, 2013)

Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. - Evan Archer; Il Memming Park; Jonathan W. Pillow (Entropy, 2013)

Kernel Methods on Spike Train Space for Neuroscience: A Tutorial. - Il Memming Park; Sohan Seth; António R. C. Paiva; Lin Li 020; José C. Príncipe (IEEE Signal Process. Mag., 2013)

Probabilistic kernel least mean squares algorithms. - Il Memming Park; Sohan Seth; Steven Van Vaerenbergh (ICASSP, 2014)

Bayesian entropy estimation for countable discrete distributions. - Evan Archer; Il Memming Park; Jonathan W. Pillow (J. Mach. Learn. Res., 2014)

Convolutional spike-triggered covariance analysis for neural subunit models. - Anqi Wu; Il Memming Park; Jonathan W. Pillow (NIPS, 2015)

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories. - Yuan Zhao 004; Il Memming Park (NIPS, 2016)

Multistep inference for generalized linear spiking models curbs runaway excitation. - David Hocker; Il Memming Park (NER, 2017)

Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains. - Yuan Zhao 004; Il Memming Park (Neural Comput., 2017)

Learning Structured Neural Dynamics From Single Trial Population Recording. - Josue Nassar; Scott W. Linderman; Yuan Zhao 004; Mónica F. Bugallo; Il Memming Park (ACSSC, 2018)

Adjoint Dynamics of Stable Limit Cycle Neural Networks. - Piotr A. Sokól; Ian D. Jordan; Eben Kadile; Il Memming Park (ACSSC, 2019)

Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. - Josue Nassar; Scott W. Linderman; Mónica F. Bugallo; Il Memming Park (ICLR (Poster), 2019)

Myopic control of neural dynamics. - David Hocker; Il Memming Park (PLoS Comput. Biol., 2019)

Jointly Learning Visual Motion and Confidence from Local Patches in Event Cameras. - Daniel R. Kepple; Daewon Lee; Colin Prepsius; Volkan Isler; Il Memming Park; Daniel D. Lee (ECCV (6), 2020)

Information Geometry of Orthogonal Initializations and Training. - Piotr Aleksander Sokól; Il Memming Park (ICLR, 2020)

Rescuing neural spike train models from bad MLE. - Diego M. Arribas; Yuan Zhao 004; Il Memming Park (NeurIPS, 2020)

On 1/n neural representation and robustness. - Josue Nassar; Piotr A. Sokól; SueYeon Chung; Kenneth D. Harris; Il Memming Park (NeurIPS, 2020)

Non-parametric generalized linear model. - Matthew Dowling; Yuan Zhao 004; Il Memming Park (CoRR, 2020)

Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed. - Ian D. Jordan; Il Memming Park (Entropy, 2020)

Variational Online Learning of Neural Dynamics. - Yuan Zhao 004; Il Memming Park (Frontiers Comput. Neurosci., 2020)

Stimulus-choice (mis)alignment in primate area MT. - Yuan Zhao 004; Jacob L. Yates; Aaron J. Levi; Alexander C. Huk; Il Memming Park (PLoS Comput. Biol., 2020)

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. - Felix Pei; Joel Ye; David M. Zoltowski; Anqi Wu; Raeed H. Chowdhury; Hansem Sohn; Joseph E. O'Doherty; Krishna V. Shenoy; Matthew T. Kaufman; Mark M. Churchland; Mehrdad Jazayeri; Lee E. Miller; et al. (NeurIPS Datasets and Benchmarks, 2021)

Hida-Matérn Kernel. - Matthew Dowling; Piotr A. Sokól; Il Memming Park (CoRR, 2021)

Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems. - Ian D. Jordan; Piotr Aleksander Sokól; Il Memming Park (Frontiers Comput. Neurosci., 2021)

Real-time variational method for learning neural trajectory and its dynamics. - Matthew Dowling; Yuan Zhao 004; Il Memming Park (ICLR, 2023)

Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains. - Matthew Dowling; Yuan Zhao 004; Il Memming Park (ICML, 2023)

Persistent learning signals and working memory without continuous attractors. - Il Memming Park; Ábel Ságodi; Piotr Aleksander Sokól (CoRR, 2023)

Streaming Variational Monte Carlo. - Yuan Zhao 004; Josue Nassar; Ian D. Jordan; Mónica F. Bugallo; Il Memming Park (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Spectral learning of Bernoulli linear dynamical systems models for decision-making. - Iris R. Stone; Yotam Sagiv; Il Memming Park; Jonathan W. Pillow (Trans. Mach. Learn. Res., 2023)

Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information. - Hyungju Jeon; Il Memming Park (EUSIPCO, 2024)

Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments. - Ayesha Vermani; Matthew Dowling; Hyungju Jeon; Ian D. Jordan; Josue Nassar; Yves Bernaerts; Yuan Zhao 004; Steven Van Vaerenbergh; Il Memming Park (EUSIPCO, 2024)

Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data. - Ayesha Vermani; Il Memming Park; Josue Nassar (ICLR, 2024)

Large-scale variational Gaussian state-space models. - Matthew Dowling; Yuan Zhao 004; Il Memming Park (CoRR, 2024)

Back to the Continuous Attractor. - Ábel Ságodi; Guillermo Martín-Sánchez; Piotr A. Sokól; Il Memming Park (CoRR, 2024)

Meta-Dynamical State Space Models for Integrative Neural Data Analysis. - Ayesha Vermani; Josue Nassar; Hyungju Jeon; Matthew Dowling; Il Memming Park (ICLR, 2025)