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Journal Articles
- 2022
[j21]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)- 2021
- [j20]Won Joon Sohn, Terence D. Sanger:
Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems. J. Comput. Neurosci. 49(2): 175-188 (2021) - 2020
- [j19]Cassie N. Borish, Matteo Bertucco, Terence D. Sanger:
Effect of target distance on controllability for myocontrol. Int. J. Hum. Comput. Stud. 140: 102432 (2020) - [j18]Terence D. Sanger, Mitsuo Kawato:
A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals. Neural Comput. 32(11): 2069-2084 (2020) - 2018
- [j17]Terence D. Sanger:
A Computational Model of Deep-Brain Stimulation for Acquired Dystonia in Children. Frontiers Comput. Neurosci. 12: 77 (2018) - 2017
- [j16]William W. Lytton, Jeff Arle, Georgiy V. Bobashev, Songbai Ji, Tara L. Klassen, Vasilis Z. Marmarelis, James S. Schwaber, Mohamed A. Sherif, Terence D. Sanger:
Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Informatics 4(4): 219-230 (2017) - 2015
- [j15]Atiyeh Ghoreyshi, Terence D. Sanger:
A Nonlinear Stochastic Filter for Continuous-Time State Estimation. IEEE Trans. Autom. Control. 60(8): 2161-2165 (2015) - 2014
- [j14]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: 141 (2014) - [j13]Terence D. Sanger:
Risk-Aware Control. Neural Comput. 26(12): 2669-2691 (2014) - 2011
- [j12]Terence D. Sanger:
Distributed Control of Uncertain Systems Using Superpositions of Linear Operators. Neural Comput. 23(8): 1911-1934 (2011) - 2004
- [j11]Terence D. Sanger:
Failure of Motor Learning for Large Initial Errors. Neural Comput. 16(9): 1873-1886 (2004) - 1998
- [j10]Terence D. Sanger:
Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons. Neural Comput. 10(6): 1567-1586 (1998) - 1994
- [j9]Terence D. Sanger:
Theoretical Considerations for the Analysis of Population Coding in Motor Cortex. Neural Comput. 6(1): 29-37 (1994) - [j8]Terence D. Sanger:
Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems. IEEE Trans. Neural Networks 5(6): 965-973 (1994) - [j7]Terence D. Sanger:
Neural network learning control of robot manipulators using gradually increasing task difficulty. IEEE Trans. Robotics Autom. 10(3): 323-333 (1994) - 1993
- [j6]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): 499-508 (1993) - 1991
- [j5]Terence D. Sanger:
Optimal Hidden Units for Two-Layer nonlinear Feedforward Neural Networks. Int. J. Pattern Recognit. Artif. Intell. 5(4): 545-562 (1991) - [j4]Terence D. Sanger:
A Tree-Structured Algorithm for Reducing Computation in Networks with Separable Basis Functions. Neural Comput. 3(1): 67-78 (1991) - [j3]Terence D. Sanger:
A tree-structured adaptive network for function approximation in high-dimensional spaces. IEEE Trans. Neural Networks 2(2): 285-293 (1991) - 1989
- [j2]Terence D. Sanger:
Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks 2(6): 459-473 (1989) - 1988
- [j1]Terence D. Sanger:
Optimal unsupervised learning. Neural Networks 1(Supplement-1): 127 (1988)
Conference and Workshop Papers
- 2024
- [c20]Khandaker Nusaiba Hafiz, Terence D. Sanger, Jonathan Realmuto:
System Identification of Human Dynamics Using Periodic Impedance Models During Physical Interaction. HAPTICS 2024: 41-46 - 2023
- [c19]Jonathan Realmuto, Jessica S. L. Vidmark, Terence D. Sanger:
Modeling deep brain stimulation evoked responses with phase oscillator networks. NER 2023: 1-4 - 2021
- [c18]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: 238-247 - [c17]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: 873-879 - 2019
- [c16]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: 53-56 - [c15]Jonathan Realmuto, Terence D. Sanger:
A robotic forearm orthosis using soft fabric-based helical actuators. RoboSoft 2019: 591-596 - 2017
- [c14]Terence D. Sanger:
Efficient parallel implementation of stochastic dynamic operators for robot control. ROBIO 2017: 1480-1484 - 2016
- [c13]Terence D. Sanger:
Learning Visually Guided Risk-Aware Reaching on a Robot Controlled by a GPU Spiking Neural Network. ICONIP (1) 2016: 282-289 - 2015
- [c12]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: 2099-2102 - 2012
- [c11]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: 37-45 - 2007
- [c10]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: 39-48 - 2006
- [c9]Johan van Doornik, Abraham K. Ishihara, Terence D. Sanger:
Uniform Boundedness of Feedback Error Learning for a Class of Stochastic Nonlinear Systems. ICARCV 2006: 1-5 - [c8]Abraham K. Ishihara, Johan van Doornik, Terence D. Sanger:
Failure Modes in Feedback Error Learning. IJCNN 2006: 277-284 - 1994
- [c7]Terence D. Sanger:
Optimal Movement Primitives. NIPS 1994: 1023-1030 - 1993
- [c6]Terence D. Sanger:
Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples. NIPS 1993: 144-151 - [c5]Terence D. Sanger:
Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements. NIPS 1993: 614-621 - 1992
- [c4]Terence D. Sanger:
A Practice Strategy for Robot Learning Control. NIPS 1992: 335-341 - 1991
- [c3]Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus:
Iterative Construction of Sparse Polynomial Approximations. NIPS 1991: 1064-1071 - 1990
- [c2]Terence D. Sanger:
Basis-Function Trees as a Generalization of Local Variable Selection Methods. NIPS 1990: 700-706 - 1988
- [c1]Terence D. Sanger:
An Optimality Principle for Unsupervised Learning. NIPS 1988: 11-19
Informal and Other Publications
- 2021
- [i1]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 abs/2107.07446 (2021)
Coauthor Index
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