Terence D. Sanger - researchr alias

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Viewing Publication 1 - 40 from 40

2023

Modeling deep brain stimulation evoked responses with phase oscillator networksJonathan Realmuto, Jessica S. L. Vidmark, Terence D. Sanger. ner 2023: 1-4 [doi]

2022

Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point ControlJonathan Realmuto, Terence D. Sanger. firai, 9, 2022. [doi]

2021

2020

Effect of target distance on controllability for myocontrolCassie N. Borish, Matteo Bertucco, Terence D. Sanger. ijmms, 140:102432, 2020. [doi]

A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time IntervalsTerence D. Sanger, Mitsuo Kawato. neco, 32(11):2069-2084, 2020. [doi]

2019

Vibro-tactile EMG-based biofeedback induces changes of muscle activity patterns in childhood dystoniaMatteo Bertucco, Francesca Lunardini, Mauro Nardon, Claudia Casellato, Alessandra Pedrocchi, Terence D. Sanger. ner 2019: 53-56 [doi]

A robotic forearm orthosis using soft fabric-based helical actuatorsJonathan Realmuto, Terence D. Sanger. robosoft 2019: 591-596 [doi]

2018

A Computational Model of Deep-Brain Stimulation for Acquired Dystonia in ChildrenTerence D. Sanger. ficn, 2018:77, 2018. [doi]

2017

Multiscale modeling in the clinic: diseases of the brain and nervous systemWilliam W. Lytton, Jeff Arle, Georgiy Bobashev, Songbai Ji, Tara L. Klassen, Vasilis Z. Marmarelis, James Schwaber, Mohamed A. Sherif, Terence D. Sanger. braininf, 4(4):219-230, 2017. [doi]

Efficient parallel implementation of stochastic dynamic operators for robot controlTerence D. Sanger. robio 2017: 1480-1484 [doi]

2016

Learning Visually Guided Risk-Aware Reaching on a Robot Controlled by a GPU Spiking Neural NetworkTerence D. Sanger. iconip 2016: 282-289 [doi]

2015

A Nonlinear Stochastic Filter for Continuous-Time State EstimationAtiyeh Ghoreyshi, Terence D. Sanger. tac, 60(8):2161-2165, 2015. [doi]

Muscle synergies in children with dystonia capture "healthy" patterns regardless the altered motor performanceFrancesca Lunardini, Claudia Casellato, Matteo Bertucco, Terence D. Sanger, Alessandra Pedrocchi. embc 2015: 2099-2102 [doi]

2014

Emulated muscle spindle and spiking afferents validates VLSI neuromorphic hardware as a testbed for sensorimotor function and diseaseChuanxin Minos Niu, Sirish K. Nandyala, Terence D. Sanger. ficn, 2014:141, 2014. [doi]

Risk-Aware ControlTerence D. Sanger. neco, 26(12):2669-2691, 2014. [doi]

2012

Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGAC. Minos Niu, Sirish Nandyala, Won Joon Sohn, Terence D. Sanger. nips 2012: 37-45 [doi]

2011

Distributed Control of Uncertain Systems Using Superpositions of Linear OperatorsTerence D. Sanger. neco, 23(8):1911-1934, 2011. [doi]

2007

A Direct Measurement of Internal Model Learning Rates in a Visuomotor Tracking TaskAbraham K. Ishihara, Johan van Doornik, Terence D. Sanger. icann 2007: 39-48 [doi]

2006

Failure Modes in Feedback Error LearningAbraham K. Ishihara, Johan van Doornik, Terence D. Sanger. ijcnn 2006: 277-284 [doi]

Uniform Boundedness of Feedback Error Learning for a Class of Stochastic Nonlinear SystemsJohan van Doornik, Abraham K. Ishihara, Terence D. Sanger. icarcv 2006: 1-5 [doi]

2004

Failure of Motor Learning for Large Initial ErrorsTerence D. Sanger. neco, 16(9):1873-1886, 2004. [doi]

1998

Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking NeuronsTerence D. Sanger. neco, 10(6):1567-1586, 1998.

1994

Optimal Movement PrimitivesTerence D. Sanger. nips 1995: 1023-1030 [doi]

Theoretical Considerations for the Analysis of Population Coding in Motor CortexTerence D. Sanger. neco, 6(1):29-37, 1994. [doi]

Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systemsTerence D. Sanger. tnn, 5(6):965-973, 1994. [doi]

Neural network learning control of robot manipulators using gradually increasing task difficultyTerence D. Sanger. trob, 10(3):323-333, 1994. [doi]

1993

Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural networkMenashe Dornay, Terence D. Sanger. bc, 68(6):499-508, 1993. [doi]

Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output SamplesTerence D. Sanger. nips 1994: 144-151 [doi]

Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head MovementsTerence D. Sanger. nips 1994: 614-621 [doi]

1992

A Practice Strategy for Robot Learning ControlTerence D. Sanger. nips 1993: 335-341 [doi]

1991

A tree-structured adaptive network for function approximation in high-dimensional spacesTerence D. Sanger. tnn, 2(2):285-293, 1991. [doi]

Optimal Hidden Units for Two-Layer nonlinear Feedforward Neural NetworksTerence D. Sanger. ijprai, 5(4):545-562, 1991. [doi]

A Tree-Structured Algorithm for Reducing Computation in Networks with Separable Basis FunctionsTerence D. Sanger. neco, 3(1):67-78, 1991. [doi]

Iterative Construction of Sparse Polynomial ApproximationsTerence D. Sanger, Richard S. Sutton, Christopher J. Matheus. nips 1992: 1064-1071 [doi]

1990

Basis-Function Trees as a Generalization of Local Variable Selection MethodsTerence D. Sanger. nips 1991: 700-706 [doi]

1989

Optimal unsupervised learning in a single-layer linear feedforward neural networkTerence D. Sanger. NN, 2(6):459-473, 1989. [doi]

1988

An Optimality Principle for Unsupervised LearningTerence D. Sanger. nips 1989: 11-19 [doi]

Optimal unsupervised learningTerence D. Sanger. NN, 1(Supplement-1):127, 1988. [doi]

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