ShiNung Ching

Neural Comput., May, 2024

Astrocytes as a mechanism for contextually-guided network dynamics and function.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2024

Synergistic pathways of modulation enable robust task packing within neural dynamics.

[BibT_eX]

[DOI]

Giacomo Vedovati

CoRR, 2024

DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

[BibT_eX]

[DOI]

Morten L. Kringelbach

NeuroImage, 2023

Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes.

[BibT_eX]

[DOI]

Lulu Gong

Xudong Chen

CoRR, 2023

Astrocytes as a mechanism for meta-plasticity and contextually-guided network function.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics.

[BibT_eX]

[DOI]

NeuroImage, 2022

Non-Stationary Representation Learning in Sequential Linear Bandits.

[BibT_eX]

[DOI]

CoRR, 2022

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement.

[BibT_eX]

[DOI]

Annu. Rev. Control., 2022

Synthesizing network dynamics for short-term memory of impulsive inputs.

[BibT_eX]

[DOI]

BethAnna Jones

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Efficient identification for modeling high-dimensional brain dynamics.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2022

Representation Learning for Context-Dependent Decision-Making.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2022

2021

Slow manifolds within network dynamics encode working memory efficiently and robustly.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2021

Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling.

[BibT_eX]

[DOI]

CoRR, 2021

Slow manifolds in recurrent networks encode working memory efficiently and robustly.

[BibT_eX]

[DOI]

CoRR, 2021

Localizing focal brain injury via EEG spectral variance.

[BibT_eX]

[DOI]

Biomed. Signal Process. Control., 2021

Top-down modeling of distributed neural dynamics for motion control.

[BibT_eX]

[DOI]

Sruti Mallik

Proceedings of the 2021 American Control Conference, 2021

2020

Synthesis of recurrent neural dynamics for monotone inclusion with application to Bayesian inference.

[BibT_eX]

[DOI]

Peng Yi

Neural Networks, 2020

Estimation and validation of individualized dynamic brain models with resting state fMRI.

[BibT_eX]

[DOI]

NeuroImage, 2020

Creating functionally favorable neural dynamics by maximizing information capacity.

[BibT_eX]

[DOI]

Neurocomputing, 2020

Learning to Control Neurons using Aggregated Measurements.

[BibT_eX]

[DOI]

Proceedings of the 2020 American Control Conference, 2020

2019

Optimal Control for Fast, Accurate Threshold-Hitting.

[BibT_eX]

[DOI]

Heinz Schättler

SIAM J. Control. Optim., 2019

Multiple Timescale Online Learning Rules for Information Maximization with Energetic Constraints.

[BibT_eX]

[DOI]

Peng Yi

Neural Comput., 2019

Biophysically interpretable inference of single neuron dynamics.

[BibT_eX]

[DOI]

Vignesh Narayanan

Jr-Shin Li

J. Comput. Neurosci., 2019

Spiking networks as efficient distributed controllers.

[BibT_eX]

[DOI]

Fuqiang Huang

Biol. Cybern., 2019

A Learning Framework for Controlling Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2019 American Control Conference, 2019

Defining information-based functional objectives for neurostimulation and control.

[BibT_eX]

[DOI]

Peng Yi

Proceedings of the 2019 American Control Conference, 2019

2018

Control Analysis and Design for Statistical Models of Spiking Networks.

[BibT_eX]

[DOI]

IEEE Trans. Control. Netw. Syst., 2018

Sensitivity of linear systems to input orientation and novelty.

[BibT_eX]

[DOI]

Autom., 2018

Network Restructuring Control for Conic Invariance with Application to Neural Networks.

[BibT_eX]

[DOI]

Matthew F. Singh

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Learning-based Approaches for Controlling Neural Spiking.

[BibT_eX]

[DOI]

Sensen Liu

Noah M. Sock

Proceedings of the 2018 Annual American Control Conference, 2018

Dynamical Spiking Networks for Distributed Control of Nonlinear Systems.

[BibT_eX]

[DOI]

Fuqiang Huang

Proceedings of the 2018 Annual American Control Conference, 2018

2017

On the Output of Nonlinear Systems Excited by Discrete Prolate Spheroidal Sequences.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2017

Bispectral analysis for measuring energy-orientation tradeoffs in the control of linear systems.

[BibT_eX]

[DOI]

Syst. Control. Lett., 2017

Recurrent networks with soft-thresholding nonlinearities for lightweight coding.

[BibT_eX]

[DOI]

Neural Networks, 2017

Recurrent Information Optimization with Local, Metaplastic Synaptic Dynamics.

[BibT_eX]

[DOI]

Sensen Liu

Neural Comput., 2017

Selective spiking in neuronal populations.

[BibT_eX]

[DOI]

Heinz Schättler

Proceedings of the 2017 American Control Conference, 2017

Optimizing the dynamics of spiking networks for decoding and control.

[BibT_eX]

[DOI]

Fuqiang Huang

James R. Riehl

Proceedings of the 2017 American Control Conference, 2017

Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury.

[BibT_eX]

[DOI]

Sina Khanmohammadi

Terranee T. Kummer

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Relating observability and compressed sensing of time-varying signals in recurrent linear networks.

[BibT_eX]

[DOI]

Neural Networks, 2016

The Geometry of Plasticity-Induced Sensitization in Isoinhibitory Rate Motifs.

[BibT_eX]

[DOI]

Neural Comput., 2016

Controlling point process generalized linear models of neural spiking.

[BibT_eX]

[DOI]

Proceedings of the 2016 American Control Conference, 2016

Endpoint-based discriminability of minimum energy inputs.

[BibT_eX]

[DOI]

Proceedings of the 2016 American Control Conference, 2016

Quasilinearization-based controllability analysis of neuronal rate networks.

[BibT_eX]

[DOI]

Seul Ah Kim

Proceedings of the 2016 American Control Conference, 2016

2015

Future challenges and opportunities in the control of neural dynamics.

[BibT_eX]

[DOI]

Jason T. Ritt

Proceedings of the American Control Conference, 2015

Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain.

[BibT_eX]

[DOI]

Jason T. Ritt

Proceedings of the American Control Conference, 2015

Controlling linear networks with minimally novel inputs.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2015

Design of optimally sparse dosing strategies for neural pharmacology.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2015

2014

A mean field model for neural-metabolic homeostatic coupling in burst suppression.

[BibT_eX]

[DOI]

Sensen Liu

Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

Maximizing relaxation time in oscillator networks with implications for neurostimulation.

[BibT_eX]

[DOI]

Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

Bounded-observation Kalman filtering of correlation in multivariate neural recordings.

[BibT_eX]

[DOI]

Ben J. Palanca

Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

Non-negative inputs for underactuated control of spiking in coupled integrate-and-fire neurons.

[BibT_eX]

[DOI]

Jason T. Ritt

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

Node selection for probing connections in evoked dynamic networks.

[BibT_eX]

[DOI]

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013

Inferring evoked brain connectivity through adaptive perturbation.

[BibT_eX]

[DOI]

Mark A. Kramer

J. Comput. Neurosci., 2013

Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression.

[BibT_eX]

[DOI]

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

An active method for tracking connectivity in temporally changing brain networks.

[BibT_eX]

[DOI]

Mark A. Kramer

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2011

Assessing neuronal interactions of cell assemblies during general anesthesia.

[BibT_eX]

[DOI]

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

2010

Simultaneous Design of Controllers and Instrumentation: ILQR/ILQG.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2010

Gaussianization of random inputs by filtering plants: The case of poisson white and telegraph processes.

[BibT_eX]

[DOI]

Thordur Runolfsson

Proceedings of the 49th IEEE Conference on Decision and Control, 2010

Modeling and control of cyclic systems in xerography.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2010

2009

Admissible Pole Locations for Tracking Random References.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2009

Root Locus for Random Reference Tracking in Systems With Saturating Actuators.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2009

Recovery of linear performance in feedback systems with nonlinear instrumentation.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2009

2007

A root locus approach to designing tracking controllers in systems with saturating actuators.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2007

2006

A switching approach to the control of jump parameter systems.

[BibT_eX]

[DOI]

Edward J. Davison

Proceedings of the American Control Conference, 2006

2005

Control of plants which change using switching controllers.

[BibT_eX]

[DOI]