Real-time statistical algorithms for controlling neural dynamics and behavior

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PI: Park, IL Memming (contact); Pillow, Jonathan William

Email: memming.park@stonybrook.edu

Institution: State University New York, Stony Brook

Title: Real-time statistical algorithms for controlling neural dynamics and behavior

Grant #: EB026946 

Status:

Deliverables:

BRAIN Math Project - Park.pptx

 

Link to Data/Model Reuse abstract, [Link] 

List of publications from the project:

  1. [1]  Hocker, D. & Park, I. M. Myopic control of neural dynamics. PLOS Computational Biology, March 2019. PMCID6428347.

  2. [2]  Nassar, J., Linderman, S. W., Bugallo, M. & Park, I. M. Tree-structured recurrent switching linear dynam- ical systems for multi-scale modeling. In International Conference on Learning Representations (ICLR), November 2019.

  3. [3]  Nassar, J., Linderman, S., Zhao, Y., Bugallo, M. & Park, I. M. Learning structured neural dynamics from single trial population recording. In 52nd Asilomar Conference on Signals, Systems and Computers, 2018.

  4. [4]  Zhao, Y., Nassar, J., Jordan, I., Bugallo, M. & Park, I. M. Streaming variational Monte Carlo. June 2019, arXiv:1906.01549 [stat.ML].

  5. [5]  Jordan, I. D. & Park, I. M. Birhythmic analog circuit maze: A nonlinear neurostimulation testbed. Entropy, 22 (5):537, May 2020, arXiv:2004.10658 [q-bio.NC]. PMCID7517031.

  6. [6]  Arribas, D. M., Zhao, Y. & Park, I. M. Rescuing neural spike train models from bad MLE. In Advances in Neural Information Processing Systems (NeurIPS), October 2020.

  7. [7]  Nassar, J., Sokol, P., Chang, S., Harris, K. & Park, I. M. On 1/n neural representation and robustness. In Advances in Neural Information Processing Systems (NeurIPS), October 2020.

  8. [8]  Jordan, I. D., Sokol, P. A. & Park, I. M. Gated recurrent units viewed through the lens of continuous time dynamical systems. Frontiers in Computational Neuroscience, 2021, arXiv:1906.01005 [cs.LG]. PM- CID8339926.

  9. [9]  Park, I. M. & Pillow, J. W. Bayesian efficient coding. July 2020. Available from: https://www.biorxiv.org/content/10.1101/178418v3, bioRxiv:pages 178418+.

  10. [10]  Zhao, Y. & Park, I. M. Variational online learning of neural dynamics. Frontiers in Computational Neuroscience, 2020, arXiv:1707.09049 [stat.ML]. PMCID7591751.

  11. [11]  Roy, N. G., Bak, J. H., Akrami, A., Brody, C. & Pillow, J. W. Efficient inference for time-varying behavior during learning. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R., editors, Advances in Neural Information Processing Systems 31, pages 5696–5706. Curran Associates, Inc., 2018.

  12. [12]  Bak, J. H. & Pillow, J. W. Adaptive stimulus selection for multi-alternative psychometric functions with lapses. Journal of Vision, 18(12):4, 2018. PMCID6222824.

  13. [13]  Aoi, M. C., Mante, V. & Pillow, J. W. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nature Neuroscience, October 2020. PMCID7610668.

  14. [14]  Keeley, S. L., Zoltowski, D. M., Aoi, M. C. & Pillow, J. W. Modeling statistical dependencies in multi-region spike train data. Current Opinion in Neurobiology, 65:194 – 202, 2020. PMCID7769979.

  15. [15]  Keeley, S. L., Zoltowski, D. M., Yu, Y., Yates, J. L., Smith, S. L. & Pillow, J. W. Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. In III, H. D. & Singh, A., editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 5177–5186, Virtual, 13–18 Jul 2020. PMLR.

  16. [16]  Keeley, S. L., Aoi, M. C., Yu, Y., Smith, S. L. & Pillow, J. W. Identifying signal and noise structure in neural population activity with gaussian process factor models. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. & Lin, H., editors, Advances in Neural Information Processing Systems 33, pages 13795–13805. Curran Associates, Inc., 2020.

  1. [17]  Zoltowski, D. M., Pillow, J. W. & Linderman, S. W. A general recurrent state space framework for modeling neural dynamics during decision-making. In III, H. D. & Singh, A., editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 11680–11691, Virtual, 13–18 Jul 2020. PMLR.

  2. [18]  Ashwood, Z. C., Roy, N. A., Stone, I. R., Churchland, A. K., Pouget, A. & Pillow, J. W. Mice alternate between discrete strategies during perceptual decision-making. 2020. Available from: https://www.biorxiv.org/content/10.1101/2020.10.19.346353v2, bioRxiv:2020.10.19.346353.

  3. [19]  Cohen, Z., DePasquale, B., Aoi, M. C. & Pillow, J. W. Recurrent dynamics of prefrontal cortex during context-dependent decision-making. 2020. Available from: https://www.biorxiv.org/content/10.1101/ 2020.11.27.401539v1.abstract, bioRxiv:2020.11.27.401539.

  4. [20]  Ashwoood, Z., Roy, N. A., Bak, J. H., Laboratory, T. I. B. & Pillow, J. W. Inferring learning rules from animal decision-making. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. & Lin, H., editors, Advances in Neural Information Processing Systems 33, pages 3442–3453. Curran Associates, Inc., 2020.

  5. [21]  Jha, A., Morais, M. J. & Pillow, J. W. Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. In Meila, M. & Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 4850–4859. PMLR, 18–24 Jul 2021.

  6. [22]  Roy, N. A., Bak, J. H., International Brain Laboratory, Akrami, A., Brody, C. D. & Pillow, J. W. Extracting the dynamics of behavior in sensory decision-making experiments. Neuron, January 2021. PMCID7897255.

  7. [23]  Kim, T. D., Luo, T. Z., Pillow, J. W. & Brody, C. Inferring latent dynamics underlying neural population activity via neural differential equations. In Meila, M. & Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 5551– 5561. PMLR, 18–24 Jul 2021.

  8. [24]  Bolkan, S. S., Stone, I. R., Pinto, L., Ashwood, Z. C., Iravedra Garcia, J. M., Herman, A. L., Singh, P., Bandi, A., Cox, J., Zimmerman, C. A., Cho, J. R., Engelhard, B., Koay, S. A., Pillow, J. W. & Witten, I. B. Strong and opponent contributions of dorsomedial striatal pathways to behavior depends on cognitive demands and task strategy. 2021. Available from: https://www.biorxiv.org/content/early/2021/07/25/2021.07.23. 453573, bioRxiv:2021.07.23.453573.

  9. [25]  Greenidge, C. D., Scholl, B., Yates, J. L. & Pillow, J. W. Efficient decoding of large-scale neural population responses with gaussian-process multiclass regression. 2021. Available from: https://www.biorxiv.org/content/early/2021/08/28/2021.08.26.457795, bioRxiv:2021.08.26.457795.

  10. [26]  Calhoun, A. J., Pillow, J. W. & Murthy, M. Unsupervised identification of the internal states that shape natural behavior. Nature Neuroscience, 22(12):2040–2049, December 2019.

 

2021 Brain PI Meeting

Update:

Link to Poster: poster

Demo:

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