Almássy N, Edelman GM, Sporns O. (1998). Behavioral constraints in the development of neuronal properties: a cortical model embedded in a real-world device. Cerebral cortex (New York, N.Y. : 1991). 8 [PubMed]
Alstermark B, Isa T. (2012). Circuits for skilled reaching and grasping. Annual review of neuroscience. 35 [PubMed]
Barrett Tech. (2012). WAM Training Documentation.
Bergenheim M, Ribot-Ciscar E, Roll JP. (2000). Proprioceptive population coding of two-dimensional limb movements in humans: I. Muscle spindle feedback during spatially oriented movements. Experimental brain research. 134 [PubMed]
Berger DJ, d'Avella A. (2014). Effective force control by muscle synergies. Frontiers in computational neuroscience. 8 [PubMed]
Carmena JM. (2013). Advances in neuroprosthetic learning and control. PLoS biology. 11 [PubMed]
Carrillo RR, Ros E, Boucheny C, Coenen OJ. (2008). A real-time spiking cerebellum model for learning robot control. Bio Systems. 94 [PubMed]
Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW. (2012). Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex. PloS one. 7 [PubMed]
Choi J, Sanchez J, Tarigoppula A, Marsh B, Chhatbar P. (2011). Control of a center-out reaching task using a reinforcement learning brain-machine interface Neural Engineering (NER), 2011 5th International IEEE-EMBS Conference on. IEEE.
DeWolf T, Eliasmith C. (2011). The neural optimal control hierarchy for motor control. Journal of neural engineering. 8 [PubMed]
Demandt E et al. (2012). Reaching movement onset- and end-related characteristics of EEG spectral power modulations. Frontiers in neuroscience. 6 [PubMed]
Dura-Bernal S, Chadderdon GL, Neymotin SA, Francis JT, Lytton WW. (2014). Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm. Pattern recognition letters. 36 [PubMed]
Edelman GM. (1987). Neural Darwinism: The Theory of Neuronal Group Selection.
Et AL et al. (1887). Evaluating hebbian reinforcement learning bmi using an in silico brain model and a virtual musculoskeletal arm Neural Control of Movement.
Fagg AH et al. (2007). Biomimetic brain machine interfaces for the control of movement. The Journal of neuroscience : the official journal of the Society for Neuroscience. 27 [PubMed]
Flint RD, Lindberg EW, Jordan LR, Miller LE, Slutzky MW. (2012). Accurate decoding of reaching movements from field potentials in the absence of spikes. Journal of neural engineering. 9 [PubMed]
Francis JT. (2009). The neural representation of kinematics and dynamics in multiple brain regions: the use of force field reaching paradigms in the primate and rat Mechanosensitivity of the Nervous System, Mechanosensitivity in Cells and Tissues. 2
Hatsopoulos N, Joshi J, O'Leary JG. (2004). Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. Journal of neurophysiology. 92 [PubMed]
Hines ML, Carnevale NT. (2001). NEURON: a tool for neuroscientists. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry. 7 [PubMed]
Hines ML, Carnevale NT. (2006). The NEURON Book.
Hogan N, Sternad D. (2009). Sensitivity of smoothness measures to movement duration, amplitude, and arrests. Journal of motor behavior. 41 [PubMed]
Holzbaur KR, Murray WM, Delp SL. (2005). A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Annals of biomedical engineering. 33 [PubMed]
Izhikevich EM. (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral cortex (New York, N.Y. : 1991). 17 [PubMed]
Kerr CC et al. (2012). Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 20 [PubMed]
Luque NR, Garrido JA, Carrillo RR, Coenen OJ, Ros E. (2011). Cerebellar input configuration toward object model abstraction in manipulation tasks. IEEE transactions on neural networks. 22 [PubMed]
Lytton W, Li K, Principe J, Francis J, Dura-Bernal S. (2015). Repairing lesions via kernel adaptive inverse control in a biomimetic model of sensorimotor cortex Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference. (Montpellier).
Lytton WW, Neymotin SA, Hines ML. (2008). The virtual slice setup. Journal of neuroscience methods. 171 [PubMed]
Lytton WW, Omurtag A. (2007). Tonic-clonic transitions in computer simulation. Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society. 24 [PubMed]
Lytton WW, Omurtag A, Neymotin SA, Hines ML. (2008). Just-in-time connectivity for large spiking networks. Neural computation. 20 [PubMed]
Lytton WW, Stewart M. (2005). A rule-based firing model for neural networks Int J Bioelectromagn. 7
Lytton WW, Stewart M. (2006). Rule-based firing for network simulations. Neurocomputing. 69
Mahmoudi B, Pohlmeyer EA, Prins NW, Geng S, Sanchez JC. (2013). Towards autonomous neuroprosthetic control using Hebbian reinforcement learning. Journal of neural engineering. 10 [PubMed]
Marsh BT, Tarigoppula VS, Chen C, Francis JT. (2015). Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning. The Journal of neuroscience : the official journal of the Society for Neuroscience. 35 [PubMed]
Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW. (2013). Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex. Neural computation. 25 [PubMed]
Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW. (2011). Emergence of physiological oscillation frequencies in a computer model of neocortex. Frontiers in computational neuroscience. 5 [PubMed]
Orin D, Featherstone R. (2000). Robot dynamics: Equations and algorithms In ICRA (International Conference Robotics and Automation).
Prins NW, Sanchez JC, Prasad A. (2014). A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces. Frontiers in neuroscience. 8 [PubMed]
Roll JP, Albert F, Ribot-Ciscar E, Bergenheim M. (2004). "Proprioceptive signature" of cursive writing in humans: a multi-population coding. Experimental brain research. 157 [PubMed]
Sanchez J et al. (2012). Dynamically repairing and replacing neural networks: using hybrid computational and biological tools. IEEE pulse. 3 [PubMed]
Sartori M, Gizzi L, Lloyd DG, Farina D. (2013). A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives. Frontiers in computational neuroscience. 7 [PubMed]
Schutte LM, Rodgers MM, Zajac F, Glaser RM. (1993). Improving the efficacy of electrical stimulation-induced leg cycle ergometry: an analysis based on a dynamic musculoskeletal model Rehabil. Eng. IEEE Trans.. 1
Shadmehr R, Mussa-Ivaldi FA. (1994). Adaptive representation of dynamics during learning of a motor task. The Journal of neuroscience : the official journal of the Society for Neuroscience. 14 [PubMed]
Song W, Kerr CC, Lytton WW, Francis JT. (2013). Cortical plasticity induced by spike-triggered microstimulation in primate somatosensory cortex. PloS one. 8 [PubMed]
Sussillo D, Churchland MM, Kaufman MT, Shenoy KV. (2015). A neural network that finds a naturalistic solution for the production of muscle activity. Nature neuroscience. 18 [PubMed]
Teulings HL, Contreras-Vidal JL, Stelmach GE, Adler CH. (1997). Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control. Experimental neurology. 146 [PubMed]
Thelen DG, Anderson FC, Delp SL. (2003). Generating dynamic simulations of movement using computed muscle control. Journal of biomechanics. 36 [PubMed]
Wolpert DM, Diedrichsen J, Flanagan JR. (2011). Principles of sensorimotor learning. Nature reviews. Neuroscience. 12 [PubMed]
Zajac FE. (1989). Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Critical reviews in biomedical engineering. 17 [PubMed]
Zhang W et al. (2014). Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models. J Comput Surg.
Dura-Bernal S et al. (2016). Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Frontiers in neuroscience. 10 [PubMed]
Lytton WW et al. (2017). Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue). 61(2/3)
Medlock L et al. (2022). Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain The Journal of neuroscience : the official journal of the Society for Neuroscience. 42 [PubMed]