An Optimality Principle for Unsupervised Learning. - Terence D. Sanger (NIPS, 1988)

Optimal unsupervised learning. - Terence D. Sanger (Neural Networks, 1988)

Optimal unsupervised learning in a single-layer linear feedforward neural network. - Terence D. Sanger (Neural Networks, 1989)

Basis-Function Trees as a Generalization of Local Variable Selection Methods. - Terence D. Sanger (NIPS, 1990)

Iterative Construction of Sparse Polynomial Approximations. - Terence D. Sanger; Richard S. Sutton; Christopher J. Matheus (NIPS, 1991)

Optimal Hidden Units for Two-Layer nonlinear Feedforward Neural Networks. - Terence D. Sanger (Int. J. Pattern Recognit. Artif. Intell., 1991)

A Tree-Structured Algorithm for Reducing Computation in Networks with Separable Basis Functions. - Terence D. Sanger (Neural Comput., 1991)

A tree-structured adaptive network for function approximation in high-dimensional spaces. - Terence D. Sanger (IEEE Trans. Neural Networks, 1991)

A Practice Strategy for Robot Learning Control. - Terence D. Sanger (NIPS, 1992)

Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples. - Terence D. Sanger (NIPS, 1993)

Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements. - Terence D. Sanger (NIPS, 1993)

Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network. - Menashe Dornay; Terence D. Sanger (Biol. Cybern., 1993)

Optimal Movement Primitives. - Terence D. Sanger (NIPS, 1994)

Theoretical Considerations for the Analysis of Population Coding in Motor Cortex. - Terence D. Sanger (Neural Comput., 1994)

Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems. - Terence D. Sanger (IEEE Trans. Neural Networks, 1994)

Neural network learning control of robot manipulators using gradually increasing task difficulty. - Terence D. Sanger (IEEE Trans. Robotics Autom., 1994)

Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons. - Terence D. Sanger (Neural Comput., 1998)

Failure of Motor Learning for Large Initial Errors. - Terence D. Sanger (Neural Comput., 2004)

Uniform Boundedness of Feedback Error Learning for a Class of Stochastic Nonlinear Systems. - Johan van Doornik; Abraham K. Ishihara; Terence D. Sanger (ICARCV, 2006)

Failure Modes in Feedback Error Learning. - Abraham K. Ishihara; Johan van Doornik; Terence D. Sanger (IJCNN, 2006)

A Direct Measurement of Internal Model Learning Rates in a Visuomotor Tracking Task. - Abraham K. Ishihara; Johan van Doornik; Terence D. Sanger (ICANN (2), 2007)

Distributed Control of Uncertain Systems Using Superpositions of Linear Operators. - Terence D. Sanger (Neural Comput., 2011)

Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA. - Chuanxin Minos Niu; Sirish K. Nandyala; Won Joon Sohn; Terence D. Sanger (NIPS, 2012)

Emulated muscle spindle and spiking afferents validates VLSI neuromorphic hardware as a testbed for sensorimotor function and disease. - Chuanxin Minos Niu; Sirish K. Nandyala; Terence D. Sanger (Frontiers Comput. Neurosci., 2014)

Risk-Aware Control. - Terence D. Sanger (Neural Comput., 2014)

Muscle synergies in children with dystonia capture "healthy" patterns regardless the altered motor performance. - Francesca Lunardini; Claudia Casellato; Matteo Bertucco; Terence D. Sanger; Alessandra Pedrocchi (EMBC, 2015)

A Nonlinear Stochastic Filter for Continuous-Time State Estimation. - Atiyeh Ghoreyshi; Terence D. Sanger (IEEE Trans. Autom. Control., 2015)

Learning Visually Guided Risk-Aware Reaching on a Robot Controlled by a GPU Spiking Neural Network. - Terence D. Sanger (ICONIP (1), 2016)

Efficient parallel implementation of stochastic dynamic operators for robot control. - Terence D. Sanger (ROBIO, 2017)

Multiscale modeling in the clinic: diseases of the brain and nervous system. - 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 (Brain Informatics, 2017)

A Computational Model of Deep-Brain Stimulation for Acquired Dystonia in Children. - Terence D. Sanger (Frontiers Comput. Neurosci., 2018)

Vibro-tactile EMG-based biofeedback induces changes of muscle activity patterns in childhood dystonia. - Matteo Bertucco; Francesca Lunardini; Mauro Nardon; Claudia Casellato; Alessandra Pedrocchi; Terence D. Sanger (NER, 2019)

A robotic forearm orthosis using soft fabric-based helical actuators. - Jonathan Realmuto; Terence D. Sanger (RoboSoft, 2019)

Effect of target distance on controllability for myocontrol. - Cassie N. Borish; Matteo Bertucco; Terence D. Sanger (Int. J. Hum. Comput. Stud., 2020)

A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals. - Terence D. Sanger; Mitsuo Kawato (Neural Comput., 2020)

Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans. - Jessica S. L. Vidmark; Estefania Hernandez-Martin; Terence D. Sanger (MICCAI (8), 2021)

Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy. - Nathaniel Dennler; Catherine Yunis; Jonathan Realmuto; Terence D. Sanger; Stefanos Nikolaidis; Maja J. Mataric (RO-MAN, 2021)

Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems. - Won Joon Sohn; Terence D. Sanger (J. Comput. Neurosci., 2021)

Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point Control. - Jonathan Realmuto; Terence D. Sanger (Frontiers Robotics AI, 2022)

Modeling deep brain stimulation evoked responses with phase oscillator networks. - Jonathan Realmuto; Jessica S. L. Vidmark; Terence D. Sanger (NER, 2023)

System Identification of Human Dynamics Using Periodic Impedance Models During Physical Interaction. - Khandaker Nusaiba Hafiz; Terence D. Sanger; Jonathan Realmuto (HAPTICS, 2024)