Learning to move machines with the mind

Trends in Neurosciences

Volumn 34, Issue 2, 2011, Pages 61-75

  Green, Andrea M ^a   Kalaska, John F ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION; ARM MOVEMENT; BRAIN COMPUTER INTERFACE; LEARNING; MOTOR CONTROL; NEUROPHYSIOLOGY; PRIORITY JOURNAL; REVIEW;

ADAPTATION, PSYCHOLOGICAL; ANIMALS; ARM; BRAIN; COMPUTER SYSTEMS; HUMANS; LEARNING; MOVEMENT; PSYCHOMOTOR PERFORMANCE; USER-COMPUTER INTERFACE;

EID: 79551599358     PISSN: 01662236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tins.2010.11.003     Document Type: Review

References (127)

1. Schwartz A.B., et al. Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 2006, 52:205-220.
2. Wolpaw J.R. Brain-computer interfaces as new brain output pathways. J. Physiol. 2007, 579:613-619.
3. Nicolelis M.A., Lebedev M.A. Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat. Rev. Neurosci. 2009, 10:530-540.
4. Hatsopoulos N.G., Donoghue J.P. The science of neural interface systems. Annu. Rev. Neurosci. 2009, 32:249-266.
5. Fetz E.E. Volitional control of neural activity: implications for brain-computer interfaces. J. Physiol. 2007, 579:571-579.
6. Donoghue J.P. Bridging the brain to the world: a perspective on neural interface systems. Neuron 2008, 60:511-521.
7. Andersen R.A., et al. Cognitive neural prosthetics. Annu. Rev. Psychol. 2010, 61:169-190. C161-163.
8. Moritz C.T., et al. Direct control of paralysed muscles by cortical neurons. Nature 2008, 456:639-642.
9. Pohlmeyer E.A., et al. Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PLoS One 2009, 4:e5924.
10. Kennedy P.R., Bakay R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 1998, 9:1707-1711.
11. Wolpaw J.R., McFarland D.J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. U. S. A. 2004, 101:17849-17854.
12. Schalk G., et al. Two-dimensional movement control using electrocorticographic signals in humans. J. Neural. Eng. 2008, 5:75-84.
13. Buch E., et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008, 39:910-917.
14. Hochberg L.R., et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442:164-171.
15. Truccolo W., et al. Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J. Neurosci. 2008, 28:1163-1178.
16. Kim S.P., et al. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural. Eng. 2008, 5:455-476.
17. Carmena J.M., et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003, 1:E42.
18. Chapin J.K., et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 1999, 2:664-670.
19. Serruya M.D., et al. Instant neural control of a movement signal. Nature 2002, 416:141-142.
20. Taylor D.M., et al. Direct cortical control of 3D neuroprosthetic devices. Science 2002, 296:1829-1832.
21. Velliste M., et al. Cortical control of a prosthetic arm for self-feeding. Nature 2008, 453:1098-1101.
22. Kalaska J.F. From intention to action: motor cortex and the control of reaching movements. Adv. Exp. Med. Biol. 2009, 629:139-178.
23. Kalaska J.F., et al. Cortical control of reaching movements. Curr. Opin. Neurobiol. 1997, 7:849-859.
24. Rizzolatti G., Luppino G. The cortical motor system. Neuron 2001, 31:889-901.
25. Buneo C.A., Andersen R.A. The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia 2006, 44:2594-2606.
26. Kakei S., et al. Sensorimotor transformations in cortical motor areas. Neurosci. Res. 2003, 46:1-10.
27. Wolpert D.M., Kawato M. Multiple paired forward and inverse models for motor control. Neural. Netw. 1998, 11:1317-1329.
28. Kawato M. Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 1999, 9:718-727.
29. Shadmehr R., Mussa-Ivaldi F.A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 1994, 14:3208-3224.
30. Shadmehr R. Generalization as a behavioral window to the neural mechanisms of learning internal models. Hum. Mov. Sci. 2004, 23:543-568.
31. Zipser D., Andersen R.A. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature 1988, 331:679-684.
32. Pouget A., Snyder L.H. Computational approaches to sensorimotor transformations. Nat. Neurosci. 2000, 3(Suppl):1192-1198.
33. Poggio T., Bizzi E. Generalization in vision and motor control. Nature 2004, 431:768-774.
34. Salinas E., Abbott L.F. Transfer of coded information from sensory to motor networks. J. Neurosci. 1995, 15:6461-6474.
35. Donchin O., et al. Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control. J. Neurosci. 2003, 23:9032-9045.
36. Thoroughman K.A., Shadmehr R. Learning of action through adaptive combination of motor primitives. Nature 2000, 407:742-747.
37. Lackner J.R., DiZio P. Motor control and learning in altered dynamic environments. Curr. Opin. Neurobiol. 2005, 15:653-659.
38. Hinder M.R., Milner T.E. Rapid adaptation to scaled changes of the mechanical environment. J. Neurophysiol. 2007, 98:3072-3080.
39. Clower D.M., et al. Role of posterior parietal cortex in the recalibration of visually guided reaching. Nature 1996, 383:618-621.
40. Kitazawa S., et al. Prism adaptation of reaching movements: specificity for the velocity of reaching. J. Neurosci. 1997, 17:1481-1492.
41. Krakauer J.W., et al. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 2000, 20:8916-8924.
42. Wang J., Sainburg R.L. Adaptation to visuomotor rotations remaps movement vectors, not final positions. J. Neurosci. 2005, 25:4024-4030.
43. Mazzoni P., Krakauer J.W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 2006, 26:3642-3645.
44. Scheidt R.A., et al. Learning to move amid uncertainty. J. Neurophysiol. 2001, 86:971-985.
45. Smith M.A., et al. Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol. 2006, 4:e179.
46. Kording K.P., et al. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat. Neurosci. 2007, 10:779-786.
47. Fine M.S., Thoroughman K.A. Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics. J. Neurophysiol. 2007, 98:1392-1404.
48. Izawa J., et al. Motor adaptation as a process of reoptimization. J. Neurosci. 2008, 28:2883-2891.
49. Berniker M., Kording K. Estimating the sources of motor errors for adaptation and generalization. Nat. Neurosci. 2008, 11:1454-1461.
50. Kluzik J., et al. Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?. J. Neurophysiol. 2008, 100:1455-1464.
51. Wei K., Kording K. Relevance of error: what drives motor adaptation?. J. Neurophysiol. 2009, 101:655-664.
52. Cothros N., et al. Are there distinct neural representations of object and limb dynamics?. Exp. Brain Res. 2006, 173:689-697.
53. van Beers R.J. Motor learning is optimally tuned to the properties of motor noise. Neuron 2009, 63:406-417.
54. Ghez C., et al. Different learned coordinate frames for planning trajectories and final positions in reaching. J. Neurophysiol. 2007, 98:3614-3626.
55. Malfait N., et al. Generalization of motor learning based on multiple field exposures and local adaptation. J. Neurophysiol. 2005, 93:3327-3338.
56. Malfait N., et al. Transfer of motor learning across arm configurations. J. Neurosci. 2002, 22:9656-9660.
57. Shadmehr R., Moussavi Z.M. Spatial generalization from learning dynamics of reaching movements. J. Neurosci. 2000, 20:7807-7815.
58. Brashers-Krug T., et al. Consolidation in human motor memory. Nature 1996, 382:252-255.
59. Shadmehr R., Brashers-Krug T. Functional stages in the formation of human long-term motor memory. J. Neurosci. 1997, 17:409-419.
60. Caithness G., et al. Failure to consolidate the consolidation theory of learning for sensorimotor adaptation tasks. J. Neurosci. 2004, 24:8662-8671.
61. Hinder M.R., et al. The interference effects of non-rotated versus counter-rotated trials in visuomotor adaptation. Exp. Brain Res. 2007, 180:629-640.
62. Krakauer J.W., et al. Adaptation to visuomotor transformations: consolidation, interference, and forgetting. J. Neurosci. 2005, 25:473-478.
63. Miall R.C., et al. Adaptation to rotated visual feedback: a re-examination of motor interference. Exp. Brain Res. 2004, 154:201-210.
64. Imamizu H., et al. Explicit contextual information selectively contributes to predictive switching of internal models. Exp. Brain Res. 2007, 181:395-408.
65. Hwang E.J., et al. A gain-field encoding of limb position and velocity in the internal model of arm dynamics. PLoS Biol. 2003, 1:E25.
66. Hwang E.J., Shadmehr R. Internal models of limb dynamics and the encoding of limb state. J. Neural. Eng. 2005, 2:S266-278.
67. Gandolfo F., et al. Motor learning by field approximation. Proc. Natl. Acad. Sci. U. S. A. 1996, 93:3843-3846.
68. Woolley D.G., et al. Dual adaptation to two opposing visuomotor rotations when each is associated with different regions of workspace. Exp. Brain Res. 2007, 179:155-165.
69. Haruno M., et al. Mosaic model for sensorimotor learning and control. Neural. Comput. 2001, 13:2201-2220.
70. Imamizu H., et al. Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models. J. Neurosci. 2004, 24:1173-1181.
71. Krouchev N.I., Kalaska J.F. Context-dependent anticipation of different task dynamics: rapid recall of appropriate motor skills using visual cues. J. Neurophysiol. 2003, 89:1165-1175.
72. Osu R., et al. Random presentation enables subjects to adapt to two opposing forces on the hand. Nat. Neurosci. 2004, 7:111-112.
73. Wada Y., et al. Acquisition and contextual switching of multiple internal models for different viscous force fields. Neurosci. Res. 2003, 46:319-331.
74. Li C.S., et al. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron 2001, 30:593-607.
75. Padoa-Schioppa C., et al. Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment. J. Neurophysiol. 2004, 91:449-473.
76. Padoa-Schioppa C., et al. Neuronal correlates of kinematics-to-dynamics transformation in the supplementary motor area. Neuron 2002, 36:751-765.
77. Xiao J., et al. Neuronal correlates of movement dynamics in the dorsal and ventral premotor area in the monkey. Exp. Brain Res. 2006, 168:106-119.
78. Arce F., et al. Neuronal correlates of memory formation in motor cortex after adaptation to force field. J. Neurosci. 2010, 30:9189-9198.
79. Arce F., et al. Combined adaptiveness of specific motor cortical ensembles underlies learning. J. Neurosci. 2010, 30:5415-5425.
80. Thoroughman K.A., Shadmehr R. Electromyographic correlates of learning an internal model of reaching movements. J. Neurosci. 1999, 19:8573-8588.
81. Paz R., et al. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nat. Neurosci. 2003, 6:882-890.
82. Paz R., Vaadia E. Learning-induced improvement in encoding and decoding of specific movement directions by neurons in the primary motor cortex. PLoS Biol. 2004, 2:E45.
83. Wolpaw J.R., et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 2002, 113:767-791.
84. Birbaumer N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 2006, 43:517-532.
85. Hwang E.J., Andersen R.A. Brain control of movement execution onset using local field potentials in posterior parietal cortex. J. Neurosci. 2009, 29:14363-14370.
86. Scherberger H., et al. Cortical local field potential encodes movement intentions in the posterior parietal cortex. Neuron 2005, 46:347-354.
87. Wang W., et al. Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2009:586-589.
88. Zhuang J., et al. Decoding 3D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Trans. Biomed. Eng. 2010, 57:1774-1784.
89. Lebedev M.A., et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci. 2005, 25:4681-4693.
90. Mulliken G.H., et al. Decoding trajectories from posterior parietal cortex ensembles. J. Neurosci. 2008, 28:12913-12926.
91. Mulliken G.H., et al. Forward estimation of movement state in posterior parietal cortex. Proc. Natl. Acad. Sci. U. S. A. 2008, 105:8170-8177.
92. Musallam S., et al. Cognitive control signals for neural prosthetics. Science 2004, 305:258-262.
93. Taylor D.M., et al. Information conveyed through brain-control: cursor versus robot. IEEE. Trans. Neural. Syst. Rehabil. Eng. 2003, 11:195-199.
94. Zacksenhouse M., et al. Cortical modulations increase in early sessions with brain-machine interface. PLoS ONE 2007, 2:e619.
95. Paninski L., et al. Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J. Neurophysiol. 2004, 91:515-532.
96. Santhanam G., et al. A high-performance brain-computer interface. Nature 2006, 442:195-198.
97. Chase S.M., et al. Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms. Neural. Netw. 2009.
98. Wahnoun R., et al. Selection and parameterization of cortical neurons for neuroprosthetic control. J. Neural. Eng. 2006, 3:162-171.
99. Tkach D., et al. Congruent activity during action and action observation in motor cortex. J. Neurosci. 2007, 27:13241-13250.
100. Tkach D., et al. Observation-based learning for brain-machine interfaces. Curr. Opin. Neurobiol. 2008, 18:589-594.
101. Cisek P., Kalaska J.F. Neural correlates of mental rehearsal in dorsal premotor cortex. Nature 2004, 431:993-996.
102. Fetz E.E. Cortical mechanisms controlling limb movement. Curr. Opin. Neurobiol. 1993, 3:932-939.
103. Reimer J., Hatsopoulos N.G. The problem of parametric neural coding in the motor system. Adv. Exp. Med. Biol. 2009, 629:243-259.
104. Churchland M.M., Shenoy K.V. Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. J. Neurophysiol. 2007, 97:4235-4257.
105. Ganguly K., Carmena J.M. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 2009, 7:e1000153.
106. Jarosiewicz B., et al. Functional network reorganization during learning in a brain-computer interface paradigm. Proc. Natl. Acad. Sci. U. S. A. 2008, 105:19486-19491.
107. Sergio L.E., et al. Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. J. Neurophysiol. 2005, 94:2353-2378.
108. Wessberg J., et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 2000, 408:361-365.
109. Wessberg J., Nicolelis M.A. Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys. J. Cogn. Neurosci. 2004, 16:1022-1035.
110. Carmena J.M., et al. Stable ensemble performance with single-neuron variability during reaching movements in primates. J. Neurosci. 2005, 25:10712-10716.
111. Chestek C.A., et al. Single-neuron stability during repeated reaching in macaque premotor cortex. J. Neurosci. 2007, 27:10742-10750.
112. Rokni U., et al. Motor learning with unstable neural representations. Neuron 2007, 54:653-666.
113. Shen L., Alexander G.E. Preferential representation of instructed target location versus limb trajectory in dorsal premotor area. J. Neurophysiol. 1997, 77:1195-1212.
114. Ochiai T., et al. Involvement of the ventral premotor cortex in controlling image motion of the hand during performance of a target-capturing task. Cereb. Cortex 2005, 15:929-937.
115. Cisek P., et al. Neural activity in primary motor and dorsal premotor cortex in reaching tasks with the contralateral versus ipsilateral arm. J. Neurophysiol. 2003, 89:922-942.
116. Davidson A.G., et al. Rapid changes in throughput from single motor cortex neurons to muscle activity. Science 2007, 318:1934-1937.
117. Fetz E.E., Baker M.A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 1973, 36:179-204.
118. Legenstein R., et al. A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J. Neurosci. 2010, 30:8400-8410.
119. Diedrichsen J., et al. Neural correlates of reach errors. J. Neurosci. 2005, 25:9919-9931.
120. Tseng Y.W., et al. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 2007, 98:54-62.
121. Desmurget M., et al. Role of the posterior parietal cortex in updating reaching movements to a visual target. Nat. Neurosci. 1999, 2:563-567.
122. Grafton S.T., et al. Neural substrates of visuomotor learning based on improved feedback control and prediction. Neuroimage 2008, 39:1383-1395.
123. Wu W., Hatsopoulos N.G. Coordinate system representations of movement direction in the premotor cortex. Exp. Brain Res. 2007, 176:652-657.
124. Gilja, V. et al. (2010) High-performance continuous neural cursor control enabled by a feedback control perspective. Conference Abstract: Computational and Systems Neuroscience 2010. doi:10.3389/conf.fnins.2010.03.00249.
125. Chase S.M., et al. Latent inputs improve estimates of neural encoding in motor cortex. J. Neurosci. 2010, 30:13873-13882.
126. Kraskov A., et al. Corticospinal neurons in macaque ventral premotor cortex with mirror properties: a potential mechanism for action suppression?. Neuron 2009, 64:922-930.
127. Stamos A.V., et al. The spinal substrate of the suppression of action during action observation. J. Neurosci. 2010, 30:11605-11611.