Terence D. Sanger

Publication Activity (10 Years)

Years Active: 1988-2024
Publications (10 Years): 18

Top Topics

Evolutionary Game

Thalamic Nuclei

Stochastic Dynamic

Top Venues

Frontiers Comput. Neurosci.

Publications

Khandaker Nusaiba Hafiz, Terence D. Sanger, Jonathan Realmuto
System Identification of Human Dynamics Using Periodic Impedance Models During Physical Interaction. HAPTICS (2024)
Jonathan Realmuto, Jessica S. L. Vidmark, Terence D. Sanger
Modeling deep brain stimulation evoked responses with phase oscillator networks. NER (2023)
Jonathan Realmuto, Terence D. Sanger
Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point Control. Future Internet 9 (2022)
Jonathan Realmuto, Terence D. Sanger
Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point Control. Frontiers Robotics AI 9 (2022)
Nathaniel Dennler, Catherine Yunis, Jonathan Realmuto, Terence D. Sanger, Stefanos Nikolaidis, Maja J. Mataric
Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy. RO-MAN (2021)
Won Joon Sohn, Terence D. Sanger
Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems. J. Comput. Neurosci. 49 (2) (2021)
Nathaniel Dennler, Catherine Yunis, Jonathan Realmuto, Terence D. Sanger, Stefanos Nikolaidis, Maja J. Mataric
Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy. CoRR (2021)
Jessica S. L. Vidmark, Estefania Hernandez-Martin, Terence D. Sanger
Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans. MICCAI (8) (2021)
Cassie N. Borish, Matteo Bertucco, Terence D. Sanger
Effect of target distance on controllability for myocontrol. Int. J. Hum. Comput. Stud. 140 (2020)
Terence D. Sanger, Mitsuo Kawato
A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals. Neural Comput. 32 (11) (2020)
Matteo Bertucco, Francesca Lunardini, Mauro Nardon, Claudia Casellato, Alessandra Pedrocchi, Terence D. Sanger
Vibro-tactile EMG-based biofeedback induces changes of muscle activity patterns in childhood dystonia. NER (2019)
Jonathan Realmuto, Terence D. Sanger
A robotic forearm orthosis using soft fabric-based helical actuators. RoboSoft (2019)
Terence D. Sanger
A Computational Model of Deep-Brain Stimulation for Acquired Dystonia in Children. Frontiers Comput. Neurosci. 12 (2018)
Terence D. Sanger
Efficient parallel implementation of stochastic dynamic operators for robot control. ROBIO (2017)
William W. Lytton, Jeff Arle, Georgiy V. Bobashev, Songbai Ji, Tara L. Klassen, Vasilis Z. Marmarelis, James Schwaber, Mohamed A. Sherif, Terence D. Sanger
Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Informatics 4 (4) (2017)
Terence D. Sanger
Learning Visually Guided Risk-Aware Reaching on a Robot Controlled by a GPU Spiking Neural Network. ICONIP (1) (2016)
Francesca Lunardini, Claudia Casellato, Matteo Bertucco, Terence D. Sanger, Alessandra Pedrocchi
Muscle synergies in children with dystonia capture "healthy" patterns regardless the altered motor performance. EMBC (2015)
Atiyeh Ghoreyshi, Terence D. Sanger
A Nonlinear Stochastic Filter for Continuous-Time State Estimation. IEEE Trans. Autom. Control. 60 (8) (2015)
Chuanxin Minos Niu, Sirish K. Nandyala, Terence D. Sanger
Emulated muscle spindle and spiking afferents validates VLSI neuromorphic hardware as a testbed for sensorimotor function and disease. Frontiers Comput. Neurosci. 8 (2014)
Terence D. Sanger
Risk-Aware Control. Neural Comput. 26 (12) (2014)
Chuanxin Minos Niu, Sirish K. Nandyala, Won Joon Sohn, Terence D. Sanger
Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA. NIPS (2012)
Terence D. Sanger
Distributed Control of Uncertain Systems Using Superpositions of Linear Operators. Neural Comput. 23 (8) (2011)
Abraham K. Ishihara, Johan van Doornik, Terence D. Sanger
A Direct Measurement of Internal Model Learning Rates in a Visuomotor Tracking Task. ICANN (2) (2007)
Johan van Doornik, Abraham K. Ishihara, Terence D. Sanger
Uniform Boundedness of Feedback Error Learning for a Class of Stochastic Nonlinear Systems. ICARCV (2006)
Abraham K. Ishihara, Johan van Doornik, Terence D. Sanger
Failure Modes in Feedback Error Learning. IJCNN (2006)
Terence D. Sanger
Failure of Motor Learning for Large Initial Errors. Neural Comput. 16 (9) (2004)
Terence D. Sanger
Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons. Neural Comput. 10 (6) (1998)
Terence D. Sanger
Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems. IEEE Trans. Neural Networks 5 (6) (1994)
Terence D. Sanger
Theoretical Considerations for the Analysis of Population Coding in Motor Cortex. Neural Comput. 6 (1) (1994)
Terence D. Sanger
Optimal Movement Primitives. NIPS (1994)
Terence D. Sanger
Neural network learning control of robot manipulators using gradually increasing task difficulty. IEEE Trans. Robotics Autom. 10 (3) (1994)
Terence D. Sanger
Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples. NIPS (1993)
Menashe Dornay, Terence D. Sanger
Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network. Biol. Cybern. 68 (6) (1993)
Terence D. Sanger
Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements. NIPS (1993)
Terence D. Sanger
A Practice Strategy for Robot Learning Control. NIPS (1992)
Terence D. Sanger
Optimal Hidden Units for Two-Layer nonlinear Feedforward Neural Networks. Int. J. Pattern Recognit. Artif. Intell. 5 (4) (1991)
Terence D. Sanger
A tree-structured adaptive network for function approximation in high-dimensional spaces. IEEE Trans. Neural Networks 2 (2) (1991)
Terence D. Sanger
A Tree-Structured Algorithm for Reducing Computation in Networks with Separable Basis Functions. Neural Comput. 3 (1) (1991)
Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus
Iterative Construction of Sparse Polynomial Approximations. NIPS (1991)
Terence D. Sanger
Basis-Function Trees as a Generalization of Local Variable Selection Methods. NIPS (1990)
Terence D. Sanger
Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks 2 (6) (1989)
Terence D. Sanger
Optimal unsupervised learning. Neural Networks 1 (Supplement-1) (1988)
Terence D. Sanger
An Optimality Principle for Unsupervised Learning. NIPS (1988)