Publications

2024 &  Pre-prints

M. F. Singh, T. Braver, M. Cole, and S. Ching, “Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations,” Accepted in PNAS. doi: 10.1101/2023.11.14.567088v1.

R. Chen, M. F. Singh, T. Braver, and S. Ching, “Resting state networks embed anatomically reliable nonlinear dynamics,” In Review.

S. Zhang, M. F. Singh, S. Ching, "Estimating uncertainty from feed-forward network based sensing using quasi-linear approximation," In Review.

Pre-2024

D. Levenstein, V. A. Alvarez, A. Amarasingham, H. Azab, R. C. Gerkin, A. Hasenstaub, R. Iyer, R. B. Jolivet, S. Marzen, J. D. Monaco, A. Prinz, S. Qurasihi, F. Santamaria, S. Shivkumar, M. F. Singh, R. Traub, H. Rotstein, F. Nadim, and A. D. Redish, “On the role of theory and modeling in neuroscience,” Journal of Neuroscience, vol. 43, no. 7, pp. 1074–1088, 2023.

M. F. Singh, M. W. Cole, T. S. Braver, and S. Ching, “Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement,” Annual Reviews in Control, vol. 54, pp. 363–376, 2022.

M. F. Singh, M. W. Cole, T. S. Braver, and S. Ching, “Control-theoretic integration of stimulation and electrophysiology for cognitive enhancement,” Frontiers in NeuroImaging, 1:982288, 2022.

M. F. Singh, M. Wang, M. W. Cole, and S. Ching, “Efficient identification for modeling high-dimensional brain dynamics,” Proceedings of the American Control Conference (ACC), pp. 1353–1358, 2022.

J. A. Etzel, R. E. Brough, M. C. Freund, A. Kizhner, Y. Lin, M. F. Singh, R. Tang, A. Tay, A. Wang, and T. S. Braver, “The dual mechanisms of cognitive control dataset, a theoretically-guided within-subject task fmri battery,” Scientific Data, vol. 9, no. 1, pp. 1–14, 2022.

M. F. Singh, A. Wang, M. Cole, S. Ching, and T. S. Braver, “Enhancing task fmri preprocessing via individualized model-based filtering of intrinsic activity dynamics,” NeuroImage, p. 118 836, 2021.

M. F. Singh, T. S. Braver, M. W. Cole, and S. Ching, “Estimation and validation of individualized dynamic brain models with resting state fMRI,” NeuroImage, vol. 221, p. 117 046, 2020.

M. F. Singh, A. Wang, T. S. Braver, and S. Ching, “Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling,” Journal of neural engineering, vol. 17, no. 4, p. 046 025, 2020.

J. R. Riehl, M. I. Zimmerman, M. F. Singh, G. R. Bowman, and S. Ching, “Computing and optimizing over all fixed-points of discrete systems on large networks,” Journal of the Royal Society Interface, vol. 17, no. 170, p. 20 200 126, 2020.

M. F. Singh and S. Ching, “Network restructuring control for conic invariance with application to neural networks,” 2018 IEEE Conference on Decision and Control (CDC), pp. 2704–2709, 2018.

M. F. Singh, T. S. Braver, and S. Ching, “Geometric classification of brain network dynamics via conic derivative discriminants,” Journal of neuroscience methods, vol. 308, pp. 88–105, 2018.

M. F. Singh and D. H. Zald, “A simple transfer function for nonlinear dendritic integration,” Frontiers in Computational Neuroscience, vol. 9, p. 98, 2015.