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<a href="bib_export/catniplab.bib">(BibTeX source)</a>
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<thead><tr><th width="20%">Author</th><th width="30%">Title</th><th width="5%">Year</th><th width="30%">Journal/Proceedings</th><th width="10%">Reftype</th><th width="5%">DOI/URL</th></tr></thead>
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<tr id="Archer2012a" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.</td>
<td>Bayesian entropy estimation for infinite neural alphabets</td>
<td>2012</td>
<td>Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
<a href="http://papers.nips.cc/paper/4521-bayesian-estimation-of-discrete-entropy-with-mixtures-of-stick-breaking-priors">URL </a>
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<tr id="Archer2012b" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.W.</td>
<td>Bayesian estimation of discrete entropy with mixtures of stick breaking priors</td>
<td>2012</td>
<td>Advances in Neural Information Processing Systems (NIPS) </td>
<td>inproceedings
</td>
<td>
<a href="https://papers.nips.cc/paper/4521-bayesian-estimation-of-discrete-entropy-with-mixtures-of-stick-breaking-priors">URL </a>
</td>
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<tr id="Archer2013b" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.</td>
<td>Semi-parametric Bayesian entropy estimation for binary spike trains</td>
<td>2013</td>
<td>Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Archer2013c" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.</td>
<td>Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data</td>
<td>2013</td>
<td>Entropy<br/>Vol. 15(5), pp. 1738-1755 </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.3390/e15051738">DOI</a>
</td>
</tr>
<tr id="Archer2013d" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.W.</td>
<td>Bayesian entropy estimation for binary spike train data using parametric prior knowledge</td>
<td>2013</td>
<td>Advances in Neural Information Processing Systems (NIPS) </td>
<td>inproceedings
</td>
<td>
<a href="http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge">URL </a>
</td>
</tr>
<tr id="Archer2014a" class="entry">
<td>Archer, E., Park, I.M. and Pillow, J.</td>
<td>Bayesian Entropy Estimation for Countable Discrete Distributions</td>
<td>2014</td>
<td>Journal of Machine Learning Research<br/>Vol. 15, pp. 2833-2868 </td>
<td>article
</td>
<td>
<a href="http://jmlr.org/papers/v15/archer14a.html">URL </a>
<a href="https://arxiv.org/abs/1302.0328">arXiv</a>
</td>
</tr>
<tr id="Archer2015a" class="entry">
<td>Archer, E., Park, I.M., Buesing, L., Cunningham, J. and Paninski, L.</td>
<td>Black box variational inference for state space models</td>
<td>2015</td>
<td>ArXiv e-prints </td>
<td>unpublished
</td>
<td>
<a href="https://arxiv.org/abs/1511.07367">arXiv</a>
</td>
</tr>
<tr id="Arora2023a" class="entry">
<td>Arora, T.</td>
<td>Exploring the expressive power of latent variable models</td>
<td>2023</td>
<td><i>School</i>: Stony Brook University </td>
<td>mastersthesis
</td>
<td>
</td>
</tr>
<tr id="Arribas2020a" class="entry">
<td>Arribas, D.M., Zhao, Y. and Park, I.M.</td>
<td>Rescuing neural spike train models from bad MLE</td>
<td>2020</td>
<td>Advances in Neural Information Processing Systems (NeurIPS) </td>
<td>inproceedings
</td>
<td>
<a href="https://papers.nips.cc/paper/2020/hash/186b690e29892f137b4c34cfa40a3a4d-Abstract.html">URL </a>
<a href="https://arxiv.org/abs/2010.12362">arXiv</a>
<a href="https://github.com/catniplab/mmd-glm">code</a>
</td>
</tr>
<tr id="Arribas2021a" class="entry">
<td>Arribas, D., Zhao, Y. and Park, M.</td>
<td>Framework to generate more realistic GLM spike trains</td>
<td>2021</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Bobkov2009a" class="entry">
<td>Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J.C. and Ache, B.W.</td>
<td>Population coding within an ensemble of rhythmically active primary olfactory receptor</td>
<td>2009</td>
<td>Society for Neuroscience </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Bobkov2010a" class="entry">
<td>Bobkov, Y., Ukhanov, K., Park, I., Príncipe, J.C. and Ache, B.</td>
<td>Measuring Ensemble Activity in Lobster ORNs through Calcium Imaging</td>
<td>2010</td>
<td>Association for Chemoreception (AChemS) Annual Meeting </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Bobkov2012a" class="entry">
<td>Bobkov, Y., Park, I., Ukhanov, K., Príncipe, J.C. and Ache, B.W.</td>
<td>Cellular basis for response diversity in the olfactory periphery</td>
<td>2012</td>
<td>PLoS One<br/>Vol. 7(4), pp. e34843+ </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.1371/journal.pone.0034843">DOI</a>
</td>
</tr>
<tr id="Bobkov2018a" class="entry">
<td>Bobkov, Y., Park, I.M., Michaelis, B.T., Matthews, T., Reidenbach, M.A., Príncipe, J.C. and Ache, B.</td>
<td>Rhythmically discharging olfactory receptor neurons can encode the spatiotemporal characteristics of odor signals within complexfluid environments</td>
<td>2018</td>
<td>European Chemoreception Research Organization (ECRO) </td>
<td>inproceedings
</td>
<td>
<a href="https://coms.events/ECRO2018/data/abstracts/en/abstract_0089.html">URL </a>
</td>
</tr>
<tr id="Bobkov2019a" class="entry">
<td>Bobkov, Y., Park, I., Michaelis, B.T., Matthews, T., Reidenbach, M.A., Príncipe, J.C. and Ache, B.</td>
<td>Coding spatiotemporal characteristics of odor signals</td>
<td>2019</td>
<td>Association for Chemoreception (AChemS) Annual Meeting </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="bookch2010a" class="entry">
<td>Príncipe, J.C., Xu, J.W., Jenssen, R., Paiva, A. and Park, I.</td>
<td>A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning</td>
<td>2010</td>
<td> </td>
<td>inbook
</td>
<td>
</td>
</tr>
<tr id="Brinkman2021a" class="entry">
<td>Brinkman, B.A.W., Yan, H., Maffei, A., Park, I.M., Fontanini, A., Wang, J. and La Camera, G.</td>
<td>Metastable dynamics of neural circuits and networks</td>
<td>2022</td>
<td>Applied Physics Reviews<br/>Vol. 9(1), pp. 011313 </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.1063/5.0062603">DOI</a>
<a href="https://arxiv.org/abs/2110.03025">arXiv</a>
</td>
</tr>
<tr id="Brockmeier2010a" class="entry">
<td>Brockmeier, A.J., Park, I., Mahmoudi, B., Sanchez, J.C. and Príncipe, J.C.</td>
<td>Spatio-Temporal Clustering of Firing Rates for Neural State Estimation</td>
<td>2010</td>
<td>Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dikecligil2016" class="entry">
<td>Dikecligil, G.N., Graham, D., Park, I.M. and Fontanini, A.</td>
<td>Layer Specific Sensorimotor Activity in the Gustatory Cortex of Licking Mice</td>
<td>2016</td>
<td>Society for Neuroscience </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dikecligil2019a" class="entry">
<td>Dikecligil, G.N., Graham, D., Park, I.M. and Fontanini, A.</td>
<td>Layer and cell type specific response properties of gustatory cortex neurons in awake mice</td>
<td>2020</td>
<td>Journal of Neuroscience </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.1523/JNEUROSCI.1579-19.2020">DOI</a>
</td>
</tr>
<tr id="Dockendorf2008" class="entry">
<td>Dockendorf, K., Park, I., He, P., Príncipe, J.C. and DeMarse, T.B.</td>
<td>Liquid State Machines and Cultured Cortical Networks: The Separation Property</td>
<td>2009</td>
<td>Biosystems<br/>Vol. 95(2), pp. 90-97 </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.1016/j.biosystems.2008.08.001">DOI</a>
</td>
</tr>
<tr id="Dowling2020a" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>Non-parametric generalized linear model</td>
<td>2020</td>
<td> </td>
<td>unpublished
</td>
<td>
<a href="https://arxiv.org/abs/2009.01362">arXiv</a>
</td>
</tr>
<tr id="Dowling2021a" class="entry">
<td>Dowling, M., Zhao, Y. and Park, M.</td>
<td>NP-GLM: Nonparametric GLM</td>
<td>2021</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dowling2021b" class="entry">
<td>Dowling, M., Sokół, P. and Park, I.M.</td>
<td>Hida-Matérn Kernel</td>
<td>2021</td>
<td> </td>
<td>unpublished
</td>
<td>
<a href="http://arxiv.org/abs/2107.07098">URL </a>
<a href="https://arxiv.org/abs/2107.07098">arXiv</a>
</td>
</tr>
<tr id="Dowling2022a" class="entry">
<td>Dowling, M., Sokół, P. and Park, I.M.</td>
<td>Hida-Matérn Gaussian Processes</td>
<td>2022</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dowling2023a" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>Real-time variational method for learning neural trajectory and its dynamics</td>
<td>2023</td>
<td>International Conference on Learning Representations (ICLR) </td>
<td>inproceedings
<br/>(top 25%)</td>
<td>
<a href="https://openreview.net/forum?id=M_MvkWgQSt">URL </a>
<a href="https://iclr.cc/virtual/2023/poster/10849">youtube</a>
<a href="https://arxiv.org/abs/2305.11278">arXiv</a>
</td>
</tr>
<tr id="Dowling2023b" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>The Exponential Family Variational Kalman Filter for Real-time Neural Dynamics</td>
<td>2023</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dowling2023c" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>Linear time GPs for inferring latent trajectories from neural spike trains</td>
<td>2023</td>
<td>International Conference on Machine Learning (ICML) </td>
<td>inproceedings
</td>
<td>
<a href="https://openreview.net/forum?id=1hWB5XEUMa">URL </a>
<a href="https://arxiv.org/abs/2306.01802">arXiv</a>
</td>
</tr>
<tr id="Dowling2024a" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>XFADS: Predicting single-trial cued behavior solely from preparatory activity</td>
<td>2024</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Dowling2024b" class="entry">
<td>Dowling, M., Zhao, Y. and Park, I.M.</td>
<td>eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling</td>
<td>2024</td>
<td>Advances in Neural Information Processing Systems (NeurIPS) </td>
<td>inproceedings
</td>
<td>
<a href="https://openreview.net/forum?id=Ln8ogihZ2S">URL </a>
<a href="https://arxiv.org/abs/2403.01371">arXiv</a>
</td>
</tr>
<tr id="Esfahany2018a" class="entry">
<td>Esfahany, K., Siergiej, I., Zhao, Y. and Park, I.M.</td>
<td>Organization of neural population code in mouse visual system</td>
<td>2018</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Esfahany2018b" class="entry">
<td>Esfahany, K., Siergiej, I., Zhao, Y. and Park, I.M.</td>
<td>Organization of neural population code in mouse visual system</td>
<td>2018</td>
<td>eNeuro, pp. 0414-17 </td>
<td>article
</td>
<td>
<a href="https://doi.org/10.1523/ENEURO.0414-17.2018">DOI</a>
<a href="https://www.biorxiv.org/content/early/2018/03/25/220558">URL </a>
</td>
</tr>
<tr id="Filipe2024a" class="entry">
<td>Filipe, A.C. and Park, I.M.</td>
<td>NeuroTask: A Benchmark Dataset for Multi-Task Neural Analysis</td>
<td>2024</td>
<td>Bernstein Conference </td>
<td>inproceedings
</td>
<td>
<a href="https://doi.org/10.12751/nncn.bc2024.020">DOI</a>
<a href="https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/ccb5dd62-403b-4da0-a839-2652ab70ac28">URL </a>
<a href="https://github.com/catniplab/NeuroTask/tree/nwb">code</a>
</td>
</tr>
<tr id="Filipe2025a" class="entry">
<td>Filipe, C., Elmakki, M., Costa-Ferreira, G. and Park, I.M.</td>
<td>Conditional Diffusion Framework for Analyzing Neural Dynamics Across Multiple Contexts</td>
<td>2025</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Hocker2017a" class="entry">
<td>Hocker, D. and Park, I.M.</td>
<td>Multistep inference for generalized linear spiking models curbs runaway excitation</td>
<td>2017</td>
<td>8th International IEEE EMBS Conference On Neural Engineering, pp. 613-616 </td>
<td>inproceedings
</td>
<td>
<a href="https://doi.org/10.1109/ner.2017.8008426">DOI</a>
<a href="/publications/Hocker2017a.pdf">PDF </a>
</td>
</tr>
<tr id="Hocker2017b" class="entry">
<td>Hocker, D. and Park, I.M.</td>
<td>Instability of the generalized linear model for spike trains</td>
<td>2017</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>
</td>
</tr>
<tr id="Hocker2018a" class="entry">
<td>Hocker, D. and Park, I.M.</td>
<td>Myopic Control: A New Control Objective for Neural Population Dynamics</td>
<td>2018</td>
<td>Computational and Systems Neuroscience (COSYNE) </td>
<td>inproceedings
</td>
<td>