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2020 – today
- 2024
[j25]BethAnna Jones, Lawrence Snyder, ShiNung Ching:
Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention. Neural Comput. 36(5): 1022-1040 (2024)- [i8]Ruiqi Chen
, Giacomo Vedovati, Todd S. Braver, ShiNung Ching:
DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models. CoRR abs/2402.09735 (2024) - [i7]Giacomo Vedovati, ShiNung Ching:
Synergistic pathways of modulation enable robust task packing within neural dynamics. CoRR abs/2408.01316 (2024) - 2023
- [j24]Andrea I. Luppi, Joana Cabral, Rodrigo Cofré, Pedro A. M. Mediano, Fernando E. Rosas, Abid Y. Qureshi, Amy Kuceyeski, Enzo Tagliazucchi, Federico Raimondo, Gustavo Deco, James M. Shine, Morten L. Kringelbach, Patricio Orio, ShiNung Ching, Yonatan Sanz Perl, Michael N. Diringer, Robert D. Stevens, Jacobo Diego Sitt:
Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. NeuroImage 275: 120162 (2023) - [i6]Lulu Gong, Fabio Pasqualetti, Thomas Papouin, ShiNung Ching:
Astrocytes as a mechanism for meta-plasticity and contextually-guided network function. CoRR abs/2311.03508 (2023) - [i5]Lulu Gong, Xudong Chen, ShiNung Ching:
Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes. CoRR abs/2312.14896 (2023) - 2022
- [j23]Matthew F. Singh, Michael W. Cole, Todd S. Braver, ShiNung Ching:
Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement. Annu. Rev. Control. 54: 363-376 (2022) - [j22]Matthew F. Singh, Anxu Wang, Michael W. Cole, ShiNung Ching, Todd S. Braver:
Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics. NeuroImage 247: 118836 (2022) - [c34]Matthew F. Singh, Michael Wang, Michael W. Cole, ShiNung Ching:
Efficient identification for modeling high-dimensional brain dynamics. ACC 2022: 1353-1358 - [c33]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Representation Learning for Context-Dependent Decision-Making. ACC 2022: 2130-2135 - [c32]BethAnna Jones, ShiNung Ching:
Synthesizing network dynamics for short-term memory of impulsive inputs. CDC 2022: 6836-6841 - [i4]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Non-Stationary Representation Learning in Sequential Linear Bandits. CoRR abs/2201.04805 (2022) - [i3]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Representation Learning for Context-Dependent Decision-Making. CoRR abs/2205.05820 (2022) - 2021
- [j21]Sina Khanmohammadi, Osvaldo Laurido-Soto, Lawrence N. Eisenman, Terrance T. Kummer, ShiNung Ching:
Localizing focal brain injury via EEG spectral variance. Biomed. Signal Process. Control. 68: 102746 (2021) - [j20]Elham Ghazizadeh, ShiNung Ching:
Slow manifolds within network dynamics encode working memory efficiently and robustly. PLoS Comput. Biol. 17(9) (2021) - [c31]Sruti Mallik, ShiNung Ching:
Top-down modeling of distributed neural dynamics for motion control. ACC 2021: 2757-2762 - [i2]Elham Ghazizadeh, ShiNung Ching:
Slow manifolds in recurrent networks encode working memory efficiently and robustly. CoRR abs/2101.03163 (2021) - [i1]Matthew F. Singh, Chong Wang, Michael W. Cole, ShiNung Ching:
Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling. CoRR abs/2104.02827 (2021) - 2020
- [j19]Elham Ghazizadeh, ShiNung Ching:
Creating functionally favorable neural dynamics by maximizing information capacity. Neurocomputing 400: 285-293 (2020) - [j18]Matthew F. Singh, Todd S. Braver, Michael W. Cole, ShiNung Ching:
Estimation and validation of individualized dynamic brain models with resting state fMRI. NeuroImage 221: 117046 (2020) - [j17]Peng Yi, ShiNung Ching:
Synthesis of recurrent neural dynamics for monotone inclusion with application to Bayesian inference. Neural Networks 131: 231-241 (2020) - [c30]Yao-Chi Yu, Vignesh Narayanan, ShiNung Ching, Jr-Shin Li:
Learning to Control Neurons using Aggregated Measurements. ACC 2020: 4028-4033
2010 – 2019
- 2019
- [j16]Fuqiang Huang, ShiNung Ching:
Spiking networks as efficient distributed controllers. Biol. Cybern. 113(1-2): 179-190 (2019) - [j15]Vignesh Narayanan, Jr-Shin Li, ShiNung Ching:
Biophysically interpretable inference of single neuron dynamics. J. Comput. Neurosci. 47(1): 61-76 (2019) - [j14]Peng Yi, ShiNung Ching:
Multiple Timescale Online Learning Rules for Information Maximization with Energetic Constraints. Neural Comput. 31(5): 943-979 (2019) - [j13]Anirban Nandi, Heinz Schättler, ShiNung Ching:
Optimal Control for Fast, Accurate Threshold-Hitting. SIAM J. Control. Optim. 57(4): 2269-2291 (2019) - [c29]Vignesh Narayanan, Jason T. Ritt, Jr-Shin Li, ShiNung Ching:
A Learning Framework for Controlling Spiking Neural Networks. ACC 2019: 211-216 - [c28]Elham Ghazizadeh, Peng Yi, ShiNung Ching:
Defining information-based functional objectives for neurostimulation and control. ACC 2019: 866-871 - 2018
- [j12]Gautam Kumar, Delsin Menolascino, ShiNung Ching:
Sensitivity of linear systems to input orientation and novelty. Autom. 93: 462-468 (2018) - [j11]Anirban Nandi, Mohammadmehdi Kafashan, ShiNung Ching:
Control Analysis and Design for Statistical Models of Spiking Networks. IEEE Trans. Control. Netw. Syst. 5(3): 1146-1156 (2018) - [c27]Fuqiang Huang, ShiNung Ching:
Dynamical Spiking Networks for Distributed Control of Nonlinear Systems. ACC 2018: 1190-1195 - [c26]Sensen Liu, Noah M. Sock, ShiNung Ching:
Learning-based Approaches for Controlling Neural Spiking. ACC 2018: 2827-2832 - [c25]Matthew F. Singh, ShiNung Ching:
Network Restructuring Control for Conic Invariance with Application to Neural Networks. CDC 2018: 2704-2709 - 2017
- [j10]Sensen Liu, ShiNung Ching:
Recurrent Information Optimization with Local, Metaplastic Synaptic Dynamics. Neural Comput. 29(9): 2528-2552 (2017) - [j9]Mohammadmehdi Kafashan, ShiNung Ching:
Recurrent networks with soft-thresholding nonlinearities for lightweight coding. Neural Networks 94: 212-219 (2017) - [j8]Delsin Menolascino, ShiNung Ching:
Bispectral analysis for measuring energy-orientation tradeoffs in the control of linear systems. Syst. Control. Lett. 102: 68-73 (2017) - [j7]Kyle Q. Lepage, ShiNung Ching:
On the Output of Nonlinear Systems Excited by Discrete Prolate Spheroidal Sequences. IEEE Trans. Autom. Control. 62(11): 5780-5787 (2017) - [c24]Sina Khanmohammadi, Terranee T. Kummer, ShiNung Ching:
Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury. ACSSC 2017: 344-348 - [c23]Fuqiang Huang, James R. Riehl, ShiNung Ching:
Optimizing the dynamics of spiking networks for decoding and control. ACC 2017: 2792-2798 - [c22]Anirban Nandi, Heinz Schättler, ShiNung Ching:
Selective spiking in neuronal populations. ACC 2017: 2811-2816 - 2016
- [j6]Gautam Kumar, ShiNung Ching:
The Geometry of Plasticity-Induced Sensitization in Isoinhibitory Rate Motifs. Neural Comput. 28(9): 1889-1926 (2016) - [j5]Mohammadmehdi Kafashan, Anirban Nandi, ShiNung Ching:
Relating observability and compressed sensing of time-varying signals in recurrent linear networks. Neural Networks 83: 11-20 (2016) - [c21]Delsin Menolascino, ShiNung Ching:
Endpoint-based discriminability of minimum energy inputs. ACC 2016: 3038-3043 - [c20]Anirban Nandi, Mohammadmehdi Kafashan, ShiNung Ching:
Controlling point process generalized linear models of neural spiking. ACC 2016: 5779-5784 - [c19]Seul Ah Kim, ShiNung Ching:
Quasilinearization-based controllability analysis of neuronal rate networks. ACC 2016: 7371-7376 - 2015
- [c18]Jason T. Ritt, ShiNung Ching:
Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain. ACC 2015: 3765-3780 - [c17]Jason T. Ritt, ShiNung Ching:
Future challenges and opportunities in the control of neural dynamics. ACC 2015: 3785 - [c16]Gautam Kumar, ShiNung Ching:
Design of optimally sparse dosing strategies for neural pharmacology. ACC 2015: 5865-5870 - [c15]Gautam Kumar, Delsin Menolascino, Mohammadmehdi Kafashan, ShiNung Ching:
Controlling linear networks with minimally novel inputs. ACC 2015: 5896-5900 - 2014
- [c14]Anirban Nandi, Jason T. Ritt, ShiNung Ching:
Non-negative inputs for underactuated control of spiking in coupled integrate-and-fire neurons. CDC 2014: 3041-3046 - [c13]Mohammadmehdi Kafashan, Kyle Q. Lepage, ShiNung Ching:
Node selection for probing connections in evoked dynamic networks. CDC 2014: 6080-6085 - [c12]Sensen Liu, ShiNung Ching:
A mean field model for neural-metabolic homeostatic coupling in burst suppression. EMBC 2014: 4852-4855 - [c11]Mohammadmehdi Kafashan, Ben J. Palanca, ShiNung Ching:
Bounded-observation Kalman filtering of correlation in multivariate neural recordings. EMBC 2014: 5052-5055 - [c10]Gautam Kumar, ShiNung Ching:
Maximizing relaxation time in oscillator networks with implications for neurostimulation. EMBC 2014: 6589-6592 - 2013
- [j4]Kyle Q. Lepage, ShiNung Ching, Mark A. Kramer:
Inferring evoked brain connectivity through adaptive perturbation. J. Comput. Neurosci. 34(2): 303-318 (2013) - [c9]Kyle Q. Lepage, Mark A. Kramer, ShiNung Ching:
An active method for tracking connectivity in temporally changing brain networks. EMBC 2013: 4374-4377 - [c8]M. Brandon Westover, ShiNung Ching, Mouhsin M. Shafi, Sydney S. Cash, Emery N. Brown:
Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression. EMBC 2013: 7108-7111 - 2011
- [c7]Zhe Chen, Sujith Vijayan, ShiNung Ching, Greg Hale, Francisco J. Flores, Matthew A. Wilson, Emery N. Brown:
Assessing neuronal interactions of cell assemblies during general anesthesia. EMBC 2011: 4175-4178 - 2010
- [j3]ShiNung Ching, Pierre T. Kabamba, Semyon M. Meerkov:
Simultaneous Design of Controllers and Instrumentation: ILQR/ILQG. IEEE Trans. Autom. Control. 55(1): 217-221 (2010) - [c6]ShiNung Ching, Yongsoon Eun, Eric M. Gross, Eric S. Hamby, Pierre T. Kabamba, Semyon M. Meerkov, Amor A. Menezes:
Modeling and control of cyclic systems in xerography. ACC 2010: 4283-4288 - [c5]ShiNung Ching, Semyon M. Meerkov, Thordur Runolfsson:
Gaussianization of random inputs by filtering plants: The case of poisson white and telegraph processes. CDC 2010: 2650-2655
2000 – 2009
- 2009
- [j2]ShiNung Ching, Pierre T. Kabamba, Semyon M. Meerkov:
Root Locus for Random Reference Tracking in Systems With Saturating Actuators. IEEE Trans. Autom. Control. 54(1): 79-91 (2009) - [j1]ShiNung Ching, Pierre T. Kabamba, Semyon M. Meerkov:
Admissible Pole Locations for Tracking Random References. IEEE Trans. Autom. Control. 54(1): 168-171 (2009) - [c4]ShiNung Ching, Pierre T. Kabamba, Semyon M. Meerkov:
Recovery of linear performance in feedback systems with nonlinear instrumentation. ACC 2009: 2545-2550 - 2007
- [c3]ShiNung Ching, Pierre T. Kabamba, Semyon M. Meerkov:
A root locus approach to designing tracking controllers in systems with saturating actuators. ACC 2007: 5828-5833 - 2006
- [c2]ShiNung Ching, Edward J. Davison:
A switching approach to the control of jump parameter systems. ACC 2006: 1-6 - 2005
- [c1]ShiNung Ching, Edward J. Davison:
Control of plants which change using switching controllers. ACC 2005: 1181-1185
Coauthor Index
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last updated on 2024-10-07 21:25 CEST by the dblp team
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