1932

Abstract

Trial-to-trial variability in the execution of movements and motor skills is ubiquitous and widely considered to be the unwanted consequence of a noisy nervous system. However, recent studies have suggested that motor variability may also be a feature of how sensorimotor systems operate and learn. This view, rooted in reinforcement learning theory, equates motor variability with purposeful exploration of motor space that, when coupled with reinforcement, can drive motor learning. Here we review studies that explore the relationship between motor variability and motor learning in both humans and animal models. We discuss neural circuit mechanisms that underlie the generation and regulation of motor variability and consider the implications that this work has for our understanding of motor learning.

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2017-07-25
2024-04-13
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Literature Cited

  1. Ali F, Otchy TM, Pehlevan C, Fantana AL, Burak Y, Ölveczky BP. 2013. The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong. Neuron 80:494–506 [Google Scholar]
  2. Alpaydin E. 2014. Introduction to Machine Learning Cambridge, MA: MIT Press
  3. Andalman AS, Fee MS. 2009. A basal ganglia-forebrain circuit in the songbird biases motor output to avoid vocal errors. PNAS 106:12518–23 [Google Scholar]
  4. Anderson DJ, Perona P. 2014. Toward a science of computational ethology. Neuron 84:18–31 [Google Scholar]
  5. Aronov D, Andalman AS, Fee MS. 2008. A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320:630–34 [Google Scholar]
  6. Athalye VR, Ganguly K, Costa RM, Carmena JM. 2017. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron 93:955–70.e5 [Google Scholar]
  7. Babloyantz A, Salazar JM, Nicolis C. 1985. Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys. Lett. A 111:152–56 [Google Scholar]
  8. Baddeley RJ, Ingram HA, Miall RC. 2003. System identification applied to a visuomotor task: near-optimal human performance in a noisy changing task. J. Neurosci. 23:3066–75 [Google Scholar]
  9. Beck JM, Ma WJ, Pitkow X, Latham PE, Pouget A. 2012. Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74:30–39 [Google Scholar]
  10. Bellman R. 1957. Dynamic Programming Mineola, NY: Dover Publ.
  11. Bernshtein NA. 1967. The Co-ordination and Regulation of Movements Oxford, UK/New York: Pergamon Press
  12. Björklund A, Dunnett SB. 2007. Dopamine neuron systems in the brain: an update. Trends Neurosci 30:194–202 [Google Scholar]
  13. Bottjer SW, Miesner EA, Arnold AP. 1984. Forebrain lesions disrupt development but not maintenance of song in passerine birds. Science 224:901–3 [Google Scholar]
  14. Botvinick MM. 2012. Hierarchical reinforcement learning and decision making. Curr. Opin. Neurobiol. 22:956–62 [Google Scholar]
  15. Braun DA, Aertsen A, Wolpert DM, Mehring C. 2009. Learning optimal adaptation strategies in unpredictable motor tasks. J. Neurosci. 29:6472–78 [Google Scholar]
  16. Bruno RM, Sakmann B. 2006. Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312:1622–27 [Google Scholar]
  17. Calvin WH, Stevens CF. 1968. Synaptic noise and other sources of randomness in motoneuron interspike intervals. J. Neurophysiol. 31:574–87 [Google Scholar]
  18. Chaisanguanthum KS, Shen HH, Sabes PN. 2014. Motor variability arises from a slow random walk in neural state. J. Neurosci. 34:12071–80 [Google Scholar]
  19. Charlesworth JD, Warren TL, Brainard MS. 2012. Covert skill learning in a cortical-basal ganglia circuit. Nature 486:251–55 [Google Scholar]
  20. Churchland MM, Afshar A, Shenoy KV. 2006. A central source of movement variability. Neuron 52:1085–96 [Google Scholar]
  21. Clamann HP. 1969. Statistical analysis of motor unit firing patterns in a human skeletal muscle. Biophys. J. 9:1233–51 [Google Scholar]
  22. Cohen JD, McClure SM, Yu AJ. 2007. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos. Trans. R. Soc. B 362:933–42 [Google Scholar]
  23. Cohen RG, Sternad D. 2008. Variability in motor learning: relocating, channeling and reducing noise. Exp. Brain Res. 193:69–83 [Google Scholar]
  24. Daw ND, O'Doherty JP, Dayan P, Seymour B, Dolan RJ. 2006. Cortical substrates for exploratory decisions in humans. Nature 441:876–79 [Google Scholar]
  25. Diesmann M, Gewaltig M-O, Aertsen A. 1999. Stable propagation of synchronous spiking in cortical neural networks. Nature 402:529–33 [Google Scholar]
  26. Doyon J, Benali H. 2005. Reorganization and plasticity in the adult brain during learning of motor skills. Curr. Opin. Neurobiol. 15:161–67 [Google Scholar]
  27. Egnor SER, Branson K. 2016. Computational analysis of behavior. Annu. Rev. Neurosci. 39:217–36 [Google Scholar]
  28. Eshel N, Bukwich M, Rao V, Hemmelder V, Tian J, Uchida N. 2015. Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525:243–46 [Google Scholar]
  29. Faisal AA, Laughlin SB. 2007. Stochastic simulations on the reliability of action potential propagation in thin axons. PLOS Comput. Biol. 3:e79 [Google Scholar]
  30. Faisal AA, Selen LPJ, Wolpert DM. 2008. Noise in the nervous system. Nat. Rev. Neurosci. 9:292–303 [Google Scholar]
  31. Fee MS, Scharff C. 2010. The songbird as a model for the generation and learning of complex sequential behaviors. ILAR J 51:362–77 [Google Scholar]
  32. Fitts PM. 1954. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47:381–91 [Google Scholar]
  33. Flament D, Hore J. 1988. Relations of motor cortex neural discharge to kinematics of passive and active elbow movements in the monkey. J. Neurophysiol. 60:1268–84 [Google Scholar]
  34. Franklin S, Wolpert DM, Franklin DW. 2012. Visuomotor feedback gains upregulate during the learning of novel dynamics. J. Neurophysiol. 108:467–78 [Google Scholar]
  35. Fu M, Yu X, Lu J, Zuo Y. 2012. Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature 483:92–95 [Google Scholar]
  36. Garst-Orozco J, Babadi B, Ölveczky BP. 2015. A neural circuit mechanism for regulating vocal variability during song learning in zebra finches. eLife 3:e03697 [Google Scholar]
  37. Gershman SJ, Pesaran B, Daw ND. 2009. Human reinforcement learning subdivides structured action spaces by learning effector-specific values. J. Neurosci. 29:13524–31 [Google Scholar]
  38. Gharib A, Derby S, Roberts S. 2001. Timing and the control of variation. J. Exp. Psychol. Anim. Behav. Process. 27:165–78 [Google Scholar]
  39. Gharib A, Gade C, Roberts S. 2004. Control of variation by reward probability. J. Exp. Psychol. Anim. Behav. Process. 30:271–82 [Google Scholar]
  40. Gonzalez Castro LN, Hadjiosif AM, Hemphill MA, Smith MA. 2014. Environmental consistency determines the rate of motor adaptation. Curr. Biol. 24:1050–61 [Google Scholar]
  41. Hadjiosif AM, Smith MA. 2015. Flexible control of safety margins for action based on environmental variability. J. Neurosci. 35:9106–21 [Google Scholar]
  42. Hahnloser RH, Kozhevnikov AA, Fee MS. 2002. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419:65–70 [Google Scholar]
  43. Haith AM, Krakauer JW. 2013. Model-based and model-free mechanisms of human motor learning. Progress in Motor Control MJ Richardson, MA Riley, K Shockley 1–21 New York: Springer [Google Scholar]
  44. Haith AM, Reppert TR, Shadmehr R. 2012. Evidence for hyperbolic temporal discounting of reward in control of movements. J. Neurosci. 32:11727–36 [Google Scholar]
  45. Hamid AA, Pettibone JR, Mabrouk OS, Hetrick VL, Schmidt R. et al. 2016. Mesolimbic dopamine signals the value of work. Nat. Neurosci. 19:117–26 [Google Scholar]
  46. Hamilton AF, de C, Jones KE, Wolpert DM. 2004. The scaling of motor noise with muscle strength and motor unit number in humans. Exp. Brain Res. 157:417–30 [Google Scholar]
  47. Harris CM, Wolpert DM. 1998. Signal-dependent noise determines motor planning. Nature 394:780–84 [Google Scholar]
  48. He K, Liang Y, Abdollahi F, Bittmann MF, Kording K, Wei K. 2016. The statistical determinants of the speed of motor learning. PLOS Comput. Biol. 12:e1005023 [Google Scholar]
  49. Herzfeld DJ, Shadmehr R. 2014. Motor variability is not noise, but grist for the learning mill. Nat. Neurosci. 17:149–50 [Google Scholar]
  50. Hessler NA, Doupe AJ. 1999. Social context modulates singing-related neural activity in the songbird forebrain. Nat. Neurosci. 2:209–11 [Google Scholar]
  51. Hikosaka O, Kim HF, Yasuda M, Yamamoto S. 2014. Basal ganglia circuits for reward value-guided behavior. Annu. Rev. Neurosci. 37:289–306 [Google Scholar]
  52. Hinton GE, Osindero S, Teh Y-W. 2006. A fast learning algorithm for deep belief nets. Neural. Comput. 18:1527–54 [Google Scholar]
  53. Horikawa Y. 1991. Noise effects on spike propagation in the stochastic Hodgkin-Huxley models. Biol. Cybern. 66:19–25 [Google Scholar]
  54. Huang VS, Haith A, Mazzoni P, Krakauer JW. 2011. Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models. Neuron 70:787–801 [Google Scholar]
  55. Immelmann K. 1969. Song development in the zebra finch and other estrildid finches. Bird Vocalizations RA Hinde 61–74 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  56. Izawa J, Rane T, Donchin O, Shadmehr R. 2008. Motor adaptation as a process of reoptimization. J. Neurosci. 28:2883–91 [Google Scholar]
  57. Izawa J, Shadmehr R. 2008. On-line processing of uncertain information in visuomotor control. J. Neurosci. 28:11360–68 [Google Scholar]
  58. Izawa J, Shadmehr R. 2011. Learning from sensory and reward prediction errors during motor adaptation. PLOS Comput. Biol. 7:e1002012 [Google Scholar]
  59. Jones KE, Hamilton AF, de C, Wolpert DM. 2002. Sources of signal-dependent noise during isometric force production. J. Neurophysiol. 88:1533–44 [Google Scholar]
  60. Kaelbling LP, Littman ML, Moore AW. 1996. Reinforcement learning: a survey. arXivcs/9605103
  61. Kakei S, Hoffman DS, Strick PL. 1999. Muscle and movement representations in the primary motor cortex. Science 285:2136–39 [Google Scholar]
  62. Kang N, Shinohara M, Zatsiorsky VM, Latash ML. 2004. Learning multi-finger synergies: an uncontrolled manifold analysis. Exp. Brain Res. 157:336–50 [Google Scholar]
  63. Kao MH, Doupe AJ, Brainard MS. 2005. Contributions of an avian basal ganglia–forebrain circuit to real-time modulation of song. Nature 433:638–43 [Google Scholar]
  64. Kao MH, Wright BD, Doupe AJ. 2008. Neurons in a forebrain nucleus required for vocal plasticity rapidly switch between precise firing and variable bursting depending on social context. J. Neurosci. 28:13232–47 [Google Scholar]
  65. Katz B, Miledi R. 1970. Membrane noise produced by acetylcholine. Nature 226:962–63 [Google Scholar]
  66. Kawagoe R, Takikawa Y, Hikosaka O. 1998. Expectation of reward modulates cognitive signals in the basal ganglia. Nat. Neurosci. 1:411–16 [Google Scholar]
  67. Kojima S, Doupe AJ. 2011. Social performance reveals unexpected vocal competency in young songbirds. PNAS 108:1687–92 [Google Scholar]
  68. Kording KP, Wolpert DM. 2004. Bayesian integration in sensorimotor learning. Nature 427:244–47 [Google Scholar]
  69. Kormushev P, Calinon S, Caldwell DG. 2010. Robot motor skill coordination with EM-based reinforcement learning. 2010 IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS)3232–37
  70. Krakauer JW, Mazzoni P. 2011. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21:636–44 [Google Scholar]
  71. Lashley KS. 1933. Integrative functions of the cerebral cortex. Physiol. Rev. 13:1–42 [Google Scholar]
  72. Latash ML, Anson JG. 2006. Synergies in health and disease: relations to adaptive changes in motor coordination. Phys. Ther. 86:1151–60 [Google Scholar]
  73. Lauwereyns J, Watanabe K, Coe B, Hikosaka O. 2002. A neural correlate of response bias in monkey caudate nucleus. Nature 418:413–17 [Google Scholar]
  74. Leblois A. 2013. Social modulation of learned behavior by dopamine in the basal ganglia: insights from songbirds. J. Physiol. 107:219–29 [Google Scholar]
  75. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44 [Google Scholar]
  76. LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86:2278–324 [Google Scholar]
  77. Lee D, Seo H, Jung MW. 2012. Neural basis of reinforcement learning and decision making. Annu. Rev. Neurosci. 35:287–308 [Google Scholar]
  78. Lee H, Pham P, Largman Y, Ng AY. 2009. Unsupervised feature learning for audio classification using convolutional deep belief networks. Advances in Neural Information Processing Systems 22 Y Bengio, D Schuurmans, JD Lafferty, CKI Williams, A Culotta 1096–104 Vancouver, Can.: Neural. Inf. Proc. Syst. [Google Scholar]
  79. Lemon RN. 2008. Descending pathways in motor control. Annu. Rev. Neurosci. 31:195–218 [Google Scholar]
  80. Litwin-Kumar A, Doiron B. 2012. Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat. Neurosci. 15:1498–505 [Google Scholar]
  81. London M, Roth A, Beeren L, Hausser M, Latham PE. 2010. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466:123–27 [Google Scholar]
  82. Luo L, Callaway E, Svoboda K. 2008. Genetic dissection of neural circuits. Neuron 57:634–60 [Google Scholar]
  83. Mainen ZF, Sejnowski TJ. 1995. Reliability of spike timing in neocortical neurons. Science 268:1503 [Google Scholar]
  84. Mandelblat-Cerf Y, Paz R, Vaadia E. 2009. Trial-to-trial variability of single cells in motor cortices is dynamically modified during visuomotor adaptation. J. Neurosci. 29:15053–62 [Google Scholar]
  85. Marcos E, Pani P, Brunamonti E, Deco G, Ferraina S, Verschure P. 2013. Neural variability in premotor cortex is modulated by trial history and predicts behavioral performance. Neuron 78:249–55 [Google Scholar]
  86. Matsumoto K, Suzuki W, Tanaka K. 2003. Neuronal correlates of goal-based motor selection in the prefrontal cortex. Science 301:229–32 [Google Scholar]
  87. Miyamoto YR, Dhawale AK, Smith MA, Ölveczky BP. 2015. Investigating reward-based regulation of task-relevant motor variability in rats Presented at Soc. Neurosci. Conf., Oct. 21 Chicago:
  88. Newell KM. 1993. Variability and Motor Control Champaign, IL: Hum. Kinet. Publ.
  89. Niv Y. 2009. Reinforcement learning in the brain. J. Math. Psychol. 53:139–54 [Google Scholar]
  90. Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM. 1996. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. J. Neurosci. 16:785–807 [Google Scholar]
  91. Ölveczky BP. 2011. Motoring ahead with rodents. Curr. Opin. Neurobiol. 21:571–78 [Google Scholar]
  92. Ölveczky BP, Andalman AS, Fee MS. 2005. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLOS Biol 3:e153 [Google Scholar]
  93. Ölveczky BP, Otchy TM, Goldberg JH, Aronov D, Fee MS. 2011. Changes in the neural control of a complex motor sequence during learning. J. Neurophysiol. 106:386–97 [Google Scholar]
  94. Opris I, Lebedev M, Nelson RJ. 2011. Motor planning under unpredictable reward: modulations of movement vigor and primate striatum activity. Front. Neurosci. 5:61 [Google Scholar]
  95. Osborne LC, Lisberger SG, Bialek W. 2005. A sensory source for motor variation. Nature 437:412–16 [Google Scholar]
  96. Parr R. 1998. Reinforcement learning with hierarchies of machines. Adv. Neural Inf. Proc. Syst. 10:1043–49 [Google Scholar]
  97. Pekny SE, Izawa J, Shadmehr R. 2015. Reward-dependent modulation of movement variability. J. Neurosci. 35:4015–24 [Google Scholar]
  98. Perkel DJ. 2004. Origin of the anterior forebrain pathway. Ann. N. Y. Acad. Sci. 1016:736–48 [Google Scholar]
  99. Peters J, Schaal S. 2008. Reinforcement learning of motor skills with policy gradients. Neural Netw 21:682–97 [Google Scholar]
  100. Poddar R, Kawai R, Ölveczky BP. 2013. A fully automated high-throughput training system for rodents. PLOS ONE 8:e83171 [Google Scholar]
  101. Price B, Boutilier C. 2003. Accelerating reinforcement learning through implicit imitation. J. Artif. Intell. Res. 19:569–629 [Google Scholar]
  102. Ravbar P, Lipkind D, Parra LC, Tchernichovski O. 2012. Vocal exploration is locally regulated during song learning. J. Neurosci. 32:3422–32 [Google Scholar]
  103. Renart A, Machens CK. 2014. Variability in neural activity and behavior. Curr. Opin. Neurobiol. 25:211–20 [Google Scholar]
  104. Roesch MR, Olson CR. 2004. Neuronal activity related to reward value and motivation in primate frontal cortex. Science 304:307–10 [Google Scholar]
  105. Samejima K, Ueda Y, Doya K, Kimura M. 2005. Representation of action-specific reward values in the striatum. Science 310:1337–40 [Google Scholar]
  106. Sanes JN, Donoghue JP. 2000. Plasticity and primary motor cortex. Annu. Rev. Neurosci. 23:393–415 [Google Scholar]
  107. Scharff C, Nottebohm F. 1991. A comparative study of the behavioral deficits following lesions of various parts of the zebra finch song system: implications for vocal learning. J. Neurosci. 11:2896–913 [Google Scholar]
  108. Scheidt RA, Dingwell JB, Mussa-Ivaldi FA. 2001. Learning to move amid uncertainty. J. Neurophysiol. 86:971–85 [Google Scholar]
  109. Schneidman E, Freedman B, Segev I. 1998. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comput 10:1679–703 [Google Scholar]
  110. Scholz JP, Schöner G. 1999. The uncontrolled manifold concept: identifying control variables for a functional task. Exp. Brain Res. 126:289–306 [Google Scholar]
  111. Schultz W. 1998. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80:1–27 [Google Scholar]
  112. Schultz W, Dayan P, Montague PR. 1997. A neural substrate of prediction and reward. Science 275:1593–99 [Google Scholar]
  113. Schultz W, Tremblay L, Hollerman JR. 2003. Changes in behavior-related neuronal activity in the striatum during learning. Trends Neurosci 26:321–28 [Google Scholar]
  114. Shadmehr R, Huang HJ, Ahmed AA. 2016. A representation of effort in decision-making and motor control. Curr. Biol. 26:1929–34 [Google Scholar]
  115. Silver D, Huang A, Maddison CJ, Guez A, Sifre L. et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–89 [Google Scholar]
  116. Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv1409.1556 [cx.CV]
  117. Simpson HB, Vicario DS. 1990. Brain pathways for learned and unlearned vocalizations differ in zebra finches. J. Neurosci. 10:1541–56 [Google Scholar]
  118. Singh P, Jana S, Ghosal A, Murthy A. 2016. Exploration of joint redundancy but not task space variability facilitates supervised motor learning. PNAS 113:14414–19 [Google Scholar]
  119. Skinner BF. 1938. The Behavior of Organisms New York: Appleton-Century-Crofts
  120. Skinner BF. 1963. Operant behavior. Am. Psychol. 18:503–15 [Google Scholar]
  121. Skinner BF. 1981. Selection by consequences. Science 213:501–4 [Google Scholar]
  122. Smith MA, Ghazizadeh A, Shadmehr R. 2006. Interacting adaptive processes with different timescales underlie short-term motor learning. PLOS Biol 4:e179 [Google Scholar]
  123. Stahlman WD, Blaisdell AP. 2011. The modulation of operant variation by the probability, magnitude, and delay of reinforcement. Learn. Motiv. 42:221–36 [Google Scholar]
  124. Stahlman WD, Roberts S, Blaisdell AP. 2010. Effect of reward probability on spatial and temporal variation. J. Exp. Psychol. Anim. Behav. Process. 36:77–91 [Google Scholar]
  125. Stein RB, Gossen ER, Jones KE. 2005. Neuronal variability: noise or part of the signal?. Nat. Rev. Neurosci. 6:389–97 [Google Scholar]
  126. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  127. Tai L-H, Lee AM, Benavidez N, Bonci A, Wilbrecht L. 2012. Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value. Nat. Neurosci. 15:1281–89 [Google Scholar]
  128. Takikawa Y, Kawagoe R, Itoh H, Nakahara H, Hikosaka O. 2002. Modulation of saccadic eye movements by predicted reward outcome. Exp. Brain Res. 142:284–91 [Google Scholar]
  129. Tchernichovski O, Mitra PP, Lints T, Nottebohm F. 2001. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291:2564 [Google Scholar]
  130. Tesileanu T, Ölveczky B, Balasubramanian V. 2016. Matching tutor to student: rules and mechanisms for efficient two-stage learning in neural circuits. bioRxiv 71910. http://dx.doi.org/10.1101/071910 [Crossref]
  131. Teşileanu T, Ölveczky B, Balasubramanian V. 2017. Rules and mechanisms for efficient two-stage learning in neural circuits. eLife 6:e20944 [Google Scholar]
  132. Thorndike EL. 1898. Animal Intelligence: An Experimental Study of the Associative Processes in Animals New York: Macmillan
  133. Todorov E. 2004. Optimality principles in sensorimotor control. Nat. Neurosci. 7:907–15 [Google Scholar]
  134. Todorov E, Jordan MI. 2002. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5:1226–35 [Google Scholar]
  135. Tumer EC, Brainard MS. 2007. Performance variability enables adaptive plasticity of “crystallized” adult birdsong. Nature 450:1240–44 [Google Scholar]
  136. van Beers RJ. 2007. The sources of variability in saccadic eye movements. J. Neurosci. 27:8757–70 [Google Scholar]
  137. van Beers RJ. 2009. Motor learning is optimally tuned to the properties of motor noise. Neuron 63:406–17 [Google Scholar]
  138. van Beers RJ, Brenner E, Smeets JBJ. 2013. Random walk of motor planning in task-irrelevant dimensions. J. Neurophysiol. 109:969–77 [Google Scholar]
  139. van Beers RJ, Haggard P, Wolpert DM. 2004. The role of execution noise in movement variability. J. Neurophysiol. 91:1050–63 [Google Scholar]
  140. van Rossum MCW, O'Brien BJ, Smith RG. 2003. Effects of noise on the spike timing precision of retinal ganglion cells. J. Neurophysiol. 89:2406–19 [Google Scholar]
  141. van Vreeswijk C, Sompolinsky H. 1996. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274:1724–26 [Google Scholar]
  142. Vogels TP, Rajan K, Abbott LF. 2005. Neural network dynamics. Annu. Rev. Neurosci. 28:357–76 [Google Scholar]
  143. Wang AY, Miura K, Uchida N. 2013. The dorsomedial striatum encodes net expected return, critical for energizing performance vigor. Nat. Neurosci. 16:639–47 [Google Scholar]
  144. Wang L, Conner JM, Rickert J, Tuszynski MH. 2011. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. PNAS 108:2545–50 [Google Scholar]
  145. Warren TL, Tumer EC, Charlesworth JD, Brainard MS. 2011. Mechanisms and time course of vocal learning and consolidation in the adult songbird. J. Neurophysiol. 106:1806–21 [Google Scholar]
  146. Wei K, Koerding K. 2010. Uncertainty of feedback and state estimation determines the speed of motor adaptation. Front. Comput. Neurosci. 4:11 [Google Scholar]
  147. White JA, Rubinstein JT, Kay AR, White JA, Rubinstein JT. et al. 2000. Channel noise in neurons. Trends Neurosci 23:131–37 [Google Scholar]
  148. Woolley SC, Rajan R, Joshua M, Doupe AJ. 2014. Emergence of context-dependent variability across a basal ganglia network. Neuron 82:208–23 [Google Scholar]
  149. Wu HG, Miyamoto YR, Gonzalez Castro LN, Ölveczky BP, Smith MA. 2014. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat. Neurosci. 17:312–21 [Google Scholar]
  150. Wunderlich K, Rangel A, O'Doherty JP. 2009. Neural computations underlying action-based decision making in the human brain. PNAS 106:17199–204 [Google Scholar]
  151. Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA. et al. 2009. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature 462:915–19 [Google Scholar]
  152. Yu AC, Margoliash D. 1996. Temporal hierarchical control of singing in birds. Science 273:1871–75 [Google Scholar]
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