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David M. Zoltowski
David M. Zoltowski
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
A tensor decomposition-based approach for detecting dynamic network states from EEG
AG Mahyari, DM Zoltowski, EM Bernat, S Aviyente
IEEE Transactions on Biomedical Engineering 64 (1), 225-237, 2016
682016
Neural latents benchmark'21: evaluating latent variable models of neural population activity
F Pei, J Ye, D Zoltowski, A Wu, RH Chowdhury, H Sohn, JE O'Doherty, ...
arXiv preprint arXiv:2109.04463, 2021
572021
Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making
DM Zoltowski, KW Latimer, JL Yates, AC Huk, JW Pillow
Neuron 102 (6), 1249-1258. e10, 2019
492019
A general recurrent state space framework for modeling neural dynamics during decision-making
DM Zoltowski, JW Pillow, SW Linderman
Proceedings of the International Conference on Machine Learning (ICML), 2983 …, 2020
402020
Modeling statistical dependencies in multi-region spike train data
SL Keeley, DM Zoltowski, MC Aoi, JW Pillow
Current opinion in neurobiology 65, 194-202, 2020
352020
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
DM Zoltowski, JW Pillow
Advances in Neural Information Processing Systems, 3521-3531, 2018
272018
Sparsity-promoting optimal control of spatially-invariant systems
DM Zoltowski, N Dhingra, F Lin, MR Jovanović
2014 American Control Conference, 1255-1260, 2014
202014
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
S Keeley, D Zoltowski, Y Yu, S Smith, J Pillow
International conference on machine learning, 5177-5186, 2020
192020
SSM: Bayesian learning and inference for state space models
S Linderman, B Antin, D Zoltowski, J Glaser
Oct, 2020
152020
A graph theoretic approach to dynamic functional connectivity tracking and network state identification
DM Zoltowski, EM Bernat, S Aviyente
2014 36th Annual International Conference of the IEEE Engineering in …, 2014
92014
Unifying and generalizing models of neural dynamics during decision-making
DM Zoltowski, JW Pillow, SW Linderman
arXiv preprint arXiv:2001.04571, 2020
72020
Slice sampling reparameterization gradients
D Zoltowski, D Cai, RP Adams
Advances in Neural Information Processing Systems 34, 23532-23544, 2021
52021
Low-rank tensor decomposition based dynamic network tracking
DM Zoltowski, S Aviyente
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2014
32014
Modeling communication and switching nonlinear dynamics in multi-region neural activity
O Karniol-Tambour, DM Zoltowski, EM Diamanti, L Pinto, DW Tank, ...
bioRxiv, 2022.09. 13.507841, 2022
12022
Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems
O Karniol-Tambour, DM Zoltowski, EM Diamanti, L Pinto, CD Brody, ...
The Twelfth International Conference on Learning Representations, 2023
2023
Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics
M Bukwich, MG Campbell, D Zoltowski, L Kingsbury, MS Tomov, J Stern, ...
bioRxiv, 2023
2023
Probabilistic Methods for Modeling Decision-Making Dynamics and Identifying Structure in Neural Datasets
DM Zoltowski
Princeton University, 2022
2022
Supplementary Material: Slice Sampling Reparameterization Gradients
DM Zoltowski, D Cai, RP Adams
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