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Annual Review of Biomedical Engineering

Volume 25, 2023

Neural Plasticity in Sensorimotor Brain–Machine Interfaces

Abstract

Brain–machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.

Keyword(s): brain–machine interface, learning, motor, neural circuits, plasticity, sensory
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/content/journals/10.1146/annurev-bioeng-110220-110833
2023-06-08
2024-04-19
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Literature Cited

  1. 1.
    Pandarinath C, Nuyujukian P, Blabe CH, Sorice BL, Saab J et al. 2017. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6:e18554
     [Google Scholar]
  2. 2.
    Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. 2022. High-performance brain-to-text communication via handwriting. Nature 593:7858249–54
     [Google Scholar]
  3. 3.
    Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC et al. 2013. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381:9866557–64
     [Google Scholar]
  4. 4.
    Flesher SN, Downey JE, Weiss JM, Hughes CL, Herrera AJ et al. 2021. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 372:6544831–36
     [Google Scholar]
  5. 5.
    Wolpert D, Ghahramani Z, Jordan M 1995. An internal model for sensorimotor integration. Science 269:523214–16
     [Google Scholar]
  6. 6.
    Hein A, Held R. 1967. Dissociation of the visual placing response into elicited and guided components. Science 158:3799390–92
     [Google Scholar]
  7. 7.
    Hein A, Held R, Gower EC. 1970. Development and segmentation of visually controlled movement by selective exposure during rearing. J. Comp. Physiol. Psychol. 73:2181–87
     [Google Scholar]
  8. 8.
    Attinger A, Wang B, Keller GB. 2017. Visuomotor coupling shapes the functional development of mouse visual cortex. Cell 169:71291–302.e14
     [Google Scholar]
  9. 9.
    Krakauer JW, Hadjiosif AM, Xu J, Wong AL, Haith AM. 2019. Motor learning. Compr. Physiol. 9:2613–63
     [Google Scholar]
  10. 10.
    Bastian AJ. 2008. Understanding sensorimotor adaptation and learning for rehabilitation. Curr. Opin. Neurol. 21:6628–33
     [Google Scholar]
  11. 11.
    Henriques DYP, Cressman EK. 2012. Visuomotor adaptation and proprioceptive recalibration. J. Mot. Behav. 44:6435–44
     [Google Scholar]
  12. 12.
    Chase SM, Schwartz AB, Kass RE 2009. Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithms. Neural Netw. 22:91203–13
     [Google Scholar]
  13. 13.
    Koyama S, Chase SM, Whitford AS, Velliste M, Schwartz AB, Kass RE 2010. Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control. J. Comput. Neurosci. 29:173–87
     [Google Scholar]
  14. 14.
    Orsborn AL, Pesaran B. 2017. Parsing learning in networks using brain–machine interfaces. Curr. Opin. Neurobiol. 46:76–83
     [Google Scholar]
  15. 15.
    Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB 2008. Functional network reorganization during learning in a brain-computer interface paradigm. PNAS 105:4919486–91
     [Google Scholar]
  16. 16.
    Chase SM, Kass RE, Schwartz AB 2012. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J. Neurophysiol. 108:2624–44
     [Google Scholar]
  17. 17.
    Zhou X, Tien RN, Ravikumar S, Chase SM. 2019. Distinct types of neural reorganization during long-term learning. J. Neurophysiol. 121:41329–41
     [Google Scholar]
  18. 18.
    Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI et al. 2014. Neural constraints on learning. Nature 512:7515423–26
     [Google Scholar]
  19. 19.
    Hwang E, Bailey P, Andersen R. 2013. Volitional control of neural activity relies on the natural motor repertoire. Curr. Biol. 23:5353–61
     [Google Scholar]
  20. 20.
    Sakellaridi S, Christopoulos VN, Aflalo T, Pejsa KW, Rosario ER et al. 2019. Intrinsic variable learning for brain-machine interface control by human anterior intraparietal cortex. Neuron 102:3694–705.e3
     [Google Scholar]
  21. 21.
    Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI et al. 2018. Learning by neural reassociation. Nat. Neurosci. 21:4607–16
     [Google Scholar]
  22. 22.
    Taylor JA, Krakauer JW, Ivry RB. 2014. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34:83023–32
     [Google Scholar]
  23. 23.
    Oby ER, Golub MD, Hennig JA, Degenhart AD, Tyler-Kabara EC et al. 2019. New neural activity patterns emerge with long-term learning. PNAS 116:3015210–15
     [Google Scholar]
  24. 24.
    Yang CS, Cowan NJ, Haith AM 2021. De novo learning versus adaptation of continuous control in a manual tracking task. eLife 10:e62578
     [Google Scholar]
  25. 25.
    Fetz EE. 1969. Operant conditioning of cortical unit activity. Science 163:3870955–58
     [Google Scholar]
  26. 26.
    Moritz CT, Fetz EE. 2011. Volitional control of single cortical neurons in a brain–machine interface. J. Neural Eng. 8:025017
     [Google Scholar]
  27. 27.
    Ganguly K, Carmena JM. 2009. Emergence of a stable cortical map for neuroprosthetic control. PLOS Biol. 7:7e1000153
     [Google Scholar]
  28. 28.
    Wander JD, Blakely T, Miller KJ, Weaver KE, Johnson LA et al. 2013. Distributed cortical adaptation during learning of a brain–computer interface task. PNAS 110:2610818–23
     [Google Scholar]
  29. 29.
    Gulati T, Ramanathan DS, Wong CC, Ganguly K. 2014. Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning. Nat. Neurosci. 17:81107–13
     [Google Scholar]
  30. 30.
    Gulati T, Guo L, Ramanathan DS, Bodepudi A, Ganguly K. 2017. Neural reactivations during sleep determine network credit assignment. Nat. Neurosci. 20:91277–84
     [Google Scholar]
  31. 31.
    Dayan E, Cohen LG. 2011. Neuroplasticity subserving motor skill learning. Neuron 72:3443–54
     [Google Scholar]
  32. 32.
    Athalye VR, Ganguly K, Costa RM, Carmena JM. 2017. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron 93:4955–70.e5
     [Google Scholar]
  33. 33.
    Athalye VR, Santos FJ, Carmena JM, Costa RM. 2018. Evidence for a neural law of effect. Science 359:63791024–29
     [Google Scholar]
  34. 34.
    Peters AJ, Chen SX, Komiyama T. 2014. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510:7504263–67
     [Google Scholar]
  35. 35.
    Dhawale AK, Smith MA, Ölveczky BP. 2017. The role of variability in motor learning. Annu. Rev. Neurosci. 40:479–98
     [Google Scholar]
  36. 36.
    Koralek AC, Jin X, Long JD II, Costa RM, Carmena JM 2012. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483:7389331–35
     [Google Scholar]
  37. 37.
    Koralek A, Costa R, Carmena J. 2013. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. Neuron 79:5865–72
     [Google Scholar]
  38. 38.
    Liu Z, Schieber MH. 2020. Neuronal activity distributed in multiple cortical areas during voluntary control of the native arm or a brain-computer interface. eNeuro 7:5ENEURO.0376-20.2020
     [Google Scholar]
  39. 39.
    Clancy KB, Koralek AC, Costa RM, Feldman DE, Carmena JM. 2014. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17:6807–9
     [Google Scholar]
  40. 40.
    Arduin PJ, Fregnac Y, Shulz DE, Ego-Stengel V. 2013.. “ Master” neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task. J. Neurosci. 33:198308–20
     [Google Scholar]
  41. 41.
    Ganguly K, Dimitrov DF, Wallis JD, Carmena JM. 2011. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14:5662–67
     [Google Scholar]
  42. 42.
    Gallego JA, Makin TR, McDougle SD. 2022. Going beyond primary motor cortex to improve brain-computer interfaces. Trends Neurosci. 45:3176–83
     [Google Scholar]
  43. 43.
    Dum R, Strick PL. 2004. Motor areas in the frontal lobe: the anatomical substrate for the central control of movement. Motor Cortex in Voluntary Movements: A Distributed System for Distributed Functions A Riehle, E Vaadia 3–47. Boca Raton, FL: CRC
     [Google Scholar]
  44. 44.
    Omrani M, Kaufman MT, Hatsopoulos NG, Cheney PD. 2017. Perspectives on classical controversies about the motor cortex. J. Neurophysiol. 118:31828–48
     [Google Scholar]
  45. 45.
    Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM et al. 2003. Learning to control a brain–machine interface for reaching and grasping by primates. PLOS Biol. 1:2e42
     [Google Scholar]
  46. 46.
    Gallego JA, Perich MG, Chowdhury RH, Solla SA, Miller LE. 2020. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23:2260–70
     [Google Scholar]
  47. 47.
    Cherian A, Krucoff MO, Miller LE. 2011. Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics. J. Neurophysiol. 106:2564–75
     [Google Scholar]
  48. 48.
    Cisek P, Kalaska JF. 2005. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45:5801–14
     [Google Scholar]
  49. 49.
    Shanechi MM, Hu RC, Powers M, Wornell GW, Brown EN, Williams ZM. 2012. Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nat. Neurosci. 15:121715–22
     [Google Scholar]
  50. 50.
    Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Yu BM et al. 2012. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15:121752–57
     [Google Scholar]
  51. 51.
    Orsborn AL, Moorman H, Overduin S, Shanechi M, Dimitrov D, Carmena JM. 2014. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82:61380–93
     [Google Scholar]
  52. 52.
    Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. 2015. A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes. J. Neural Eng. 12:036009
     [Google Scholar]
  53. 53.
    Mulliken GH, Musallam S, Andersen RA. 2008. Decoding trajectories from posterior parietal cortex ensembles. J. Neurosci. 28:4812913–26
     [Google Scholar]
  54. 54.
    Whitlock JR. 2017. Posterior parietal cortex. Curr. Biol. 27:14R691–95
     [Google Scholar]
  55. 55.
    Andersen RA, Buneo CA. 2002. Intentional maps in posterior parietal cortex. Annu. Rev. Neurosci. 25:189–220
     [Google Scholar]
  56. 56.
    Batista AP, Buneo CA, Snyder LH, Andersen RA. 2022. Reach plans in eye-centered coordinates. Science 285:5425257–60
     [Google Scholar]
  57. 57.
    Neely RM, Koralek AC, Athalye VR, Costa RM, Carmena JM. 2018. Volitional modulation of primary visual cortex activity requires the basal ganglia. Neuron 97:61356–68.e4
     [Google Scholar]
  58. 58.
    Musall S, Kaufman MT, Juavinett AL, Gluf S, Churchland AK. 2019. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22:101677–86
     [Google Scholar]
  59. 59.
    Ganguly K, Secundo L, Ranade G, Orsborn AL, Chang EF et al. 2009. Cortical representation of ipsilateral arm movements in monkey and man. J. Neurosci. 29:4112948–56
     [Google Scholar]
  60. 60.
    Mahmoudi B, Sanchez JC. 2011. A symbiotic brain-machine interface through value-based decision making. PLOS ONE 6:3e14760
     [Google Scholar]
  61. 61.
    Silversmith DB, Abiri R, Hardy NF, Natraj N, Tu-Chan A et al. 2021. Plug-and-play control of a brain–computer interface through neural map stabilization. Nat. Biotechnol. 39:3326–35
     [Google Scholar]
  62. 62.
    Lu HY, Lorenc ES, Zhu H, Kilmarx J, Sulzer J et al. 2021. Multi-scale neural decoding and analysis. J. Neural Eng. 18:045013
     [Google Scholar]
  63. 63.
    Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H et al. 2020. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat. Biomed. Eng. 4:10973–83
     [Google Scholar]
  64. 64.
    Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT et al. 2019. Accurate estimation of neural population dynamics without spike sorting. Neuron 103:2292–308.e4
     [Google Scholar]
  65. 65.
    Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A et al. 2021. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nat. Commun. 12:3689
     [Google Scholar]
  66. 66.
    Mitani A, Dong M, Komiyama T. 2018. Brain-computer interface with inhibitory neurons reveals subtype-specific strategies. Curr. Biol. 28:177–83.e4
     [Google Scholar]
  67. 67.
    Clancy KB, Mrsic-Flogel TD. 2012. The sensory representation of causally controlled objects. Neuron 109:4677–89.e4
     [Google Scholar]
  68. 68.
    Prsa M, Galiñanes GL, Huber D. 2017. Rapid integration of artificial sensory feedback during operant conditioning of motor cortex neurons. Neuron 93:4929–39.e6
     [Google Scholar]
  69. 69.
    Buzsáki G, Anastassiou CA, Koch C. 2012. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13:6407–20
     [Google Scholar]
  70. 70.
    So K, Dangi S, Orsborn AL, Gastpar MC, Carmena JM. 2014. Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates. J. Neural Eng. 11:026002
     [Google Scholar]
  71. 71.
    Flint RD, Wright ZA, Scheid MR, Slutzky MW. 2013. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10:056005
     [Google Scholar]
  72. 72.
    Engelhard B, Ozeri N, Israel Z, Bergman H, Vaadia E. 2013. Inducing γ oscillations and precise spike synchrony by operant conditioning via brain–machine interface. Neuron 77:2361–75
     [Google Scholar]
  73. 73.
    Benabid AL, Costecalde T, Eliseyev A, Charvet G, Verney A et al. 2019. An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol. 18:121112–22
     [Google Scholar]
  74. 74.
    Hwang EJ, Andersen RA. 2009. Brain control of movement execution onset using local field potentials in posterior parietal cortex. J. Neurosci. 29:4514363–70
     [Google Scholar]
  75. 75.
    Degenhart AD, Bishop WE, Oby ER, Tyler-Kabara EC, Chase SM et al. 2020. Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity. Nat. Biomed. Eng. 4:7672–85
     [Google Scholar]
  76. 76.
    Shanechi MM, Orsborn AL, Moorman HG, Gowda S, Dangi S, Carmena JM. 2017. Rapid control and feedback rates enhance neuroprosthetic control. Nat. Commun. 8:13825
     [Google Scholar]
  77. 77.
    Willett FR, Suminski AJ, Fagg AH, Hatsopoulos NG. 2013. Improving brain–machine interface performance by decoding intended future movements. J. Neural Eng. 10:026011
     [Google Scholar]
  78. 78.
    Wolpert DM, Flanagan JR. 2001. Motor prediction. Curr. Biol. 11:18R729–32
     [Google Scholar]
  79. 79.
    Kao JC, Ryu SI, Shenoy KV. 2017. Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Sci. Rep. 7:7395
     [Google Scholar]
  80. 80.
    Law AJ, Rivlis G, Schieber MH. 2014. Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. J. Neurophysiol. 112:61528–48
     [Google Scholar]
  81. 81.
    Tremblay R, Lee S, Rudy B 2016. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91:2260–92
     [Google Scholar]
  82. 82.
    Makino H, Hwang EJ, Hedrick NG, Komiyama T. 2016. Circuit mechanisms of sensorimotor learning. Neuron 92:4705–21
     [Google Scholar]
  83. 83.
    Markowitz DA, Wong YT, Gray CM, Pesaran B. 2011. Optimizing the decoding of movement goals from local field potentials in macaque cortex. J. Neurosci. 31:5018412–22
     [Google Scholar]
  84. 84.
    Garcia-Garcia MG, Marquez-Chin C, Popovic MR. 2020. Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task. Sci. Rep. 10:19992
     [Google Scholar]
  85. 85.
    Vendrell-Llopis N, Fang C, Qü AJ, Costa RM, Carmena JM. 2022. Diverse operant control of different motor cortex populations during learning. Curr. Biol. 32:71616–22.e5
     [Google Scholar]
  86. 86.
    Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ. 2006. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput. 18:180–118
     [Google Scholar]
  87. 87.
    Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD et al. 2012. A recurrent neural network for closed-loop intracortical brain–machine interface decoders. J. Neural Eng. 9:026027
     [Google Scholar]
  88. 88.
    Willsey MS, Nason-Tomaszewski SR, Ensel SR, Temmar H, Mender MJ et al. 2022. Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder. Nat. Commun. 13:6899
     [Google Scholar]
  89. 89.
    Taylor DM, Tillery SIH, Schwartz AB. 2002. Direct cortical control of 3D neuroprosthetic devices. Science 296:55741829–32
     [Google Scholar]
  90. 90.
    Orsborn AL, Dangi S, Moorman HG, Carmena JM. 2012. Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Trans. Neural Syst. Rehabil. Eng. 20:4468–77
     [Google Scholar]
  91. 91.
    Brandman DM, Hosman T, Saab J, Burkhart MC, Shanahan BE et al. 2018. Rapid calibration of an intracortical brain-computer interface for people with tetraplegia. J. Neural Eng. 15:026007
     [Google Scholar]
  92. 92.
    Li Z, O'Doherty JE, Lebedev MA, Nicolelis MAL. 2011. Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Comput. 23:123162–204
     [Google Scholar]
  93. 93.
    Madduri MM, Burden SA, Orsborn AL. 2021. A game-theoretic model for co-adaptive brain-machine interfaces. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)327–30. Piscataway, NJ: IEEE
     [Google Scholar]
  94. 94.
    Marathe AR, Taylor DM. 2011. Decoding position, velocity, or goal: Does it matter for brain–machine interfaces?. J. Neural Eng. 8:025016
     [Google Scholar]
  95. 95.
    Golub MD, Yu BM, Chase SM 2015. Internal models for interpreting neural population activity during sensorimotor control. eLife 4:e10015
     [Google Scholar]
  96. 96.
    Gowda S, Orsborn AL, Overduin SA, Moorman HG, Carmena JM. 2014. Designing dynamical properties of brain–machine interfaces to optimize task-specific performance. IEEE Trans. Neural Syst. Rehabil. Eng. 22:5911–20
     [Google Scholar]
  97. 97.
    Zhang Y, Chase SM. 2015. Recasting brain-machine interface design from a physical control system perspective. J. Comput. Neurosci. 39:2107–18
     [Google Scholar]
  98. 98.
    Blakemore SJ, Frith CD, Wolpert DM. 1999. Spatio-temporal prediction modulates the perception of self-produced stimuli. J. Cogn. Neurosci. 11:5551–59
     [Google Scholar]
  99. 99.
    Eaton RW, Libey T, Fetz EE. 2017. Operant conditioning of neural activity in freely behaving monkeys with intracranial reinforcement. J. Neurophysiol. 117:31112–25
     [Google Scholar]
  100. 100.
    Suminski AJ, Tkach DC, Fagg AH, Hatsopoulos NG. 2010. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30:5016777–87
     [Google Scholar]
  101. 101.
    Johansson RS, Flanagan JR. 2009. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10:5345–59
     [Google Scholar]
  102. 102.
    Delhaye BP, Long KH, Bensmaia SJ. 2018. Neural basis of touch and proprioception. Compr. Physiol. 8:41575–602
     [Google Scholar]
  103. 103.
    Sainburg RL, Poizner H, Ghez C. 1993. Loss of proprioception produces deficits in interjoint coordination. J. Neurophysiol. 70:52136–47
     [Google Scholar]
  104. 104.
    Raspopovic S, Capogrosso M, Petrini FM, Bonizzato M, Rigosa J et al. 2014. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6:222ra19
     [Google Scholar]
  105. 105.
    Penfield W, Boldrey E. 1937. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60:4389–443
     [Google Scholar]
  106. 106.
    Histed MH, Ni AM, Maunsell JHR. 2013. Insights into cortical mechanisms of behavior from microstimulation experiments. Prog. Neurobiol. 103:115–30
     [Google Scholar]
  107. 107.
    Cohen MR, Newsome WT. 2004. What electrical microstimulation has revealed about the neural basis of cognition. Curr. Opin. Neurobiol. 14:2169–77
     [Google Scholar]
  108. 108.
    Roche JP, Hansen MR. 2015. On the horizon: cochlear implant technology. Otolaryngol. Clin. N. Am. 48:61097–116
     [Google Scholar]
  109. 109.
    Murphey DK, Maunsell JH. 2007. Behavioral detection of electrical microstimulation in different cortical visual areas. Curr. Biol. 17:10862–67
     [Google Scholar]
  110. 110.
    Doty RW. 1965. Conditioned reflexes elicited by electrical stimulation of the brain in macaques. J. Neurophysiol. 28:623–40
     [Google Scholar]
  111. 111.
    Dadarlat MC, O'Doherty JE, Sabes PN 2015. A learning-based approach to artificial sensory feedback leads to optimal integration. Nat. Neurosci. 18:1138–44
     [Google Scholar]
  112. 112.
    Tabot GA, Dammann JF, Berg JA, Tenore FV, Boback JL et al. 2013. Restoring the sense of touch with a prosthetic hand through a brain interface. PNAS 110:4518279–84
     [Google Scholar]
  113. 113.
    London BM, Jordan LR, Jackson CR, Miller LE. 2008. Electrical stimulation of the proprioceptive cortex (area 3a) used to instruct a behaving monkey. IEEE Trans. Neural Syst. Rehabil. Eng. 16:132–36
     [Google Scholar]
  114. 114.
    O'Doherty JE, Shokur S, Medina LE, Lebedev MA, Nicolelis MA. 2019. Creating a neuroprosthesis for active tactile exploration of textures. PNAS 116:4321821–27
     [Google Scholar]
  115. 115.
    Flesher SN, Collinger JL, Foldes ST, Weiss JM, Downey JE et al. 2016. Intracortical microstimulation of human somatosensory cortex. Sci. Transl. Med. 8:361ra141
     [Google Scholar]
  116. 116.
    Stoney SD, Thompson WD, Asanuma H. 1968. Excitation of pyramidal tract cells by intracortical microstimulation: effective extent of stimulating current. J. Neurophysiol. 31:5659–69
     [Google Scholar]
  117. 117.
    Butovas S, Schwarz C. 2003. Spatiotemporal effects of microstimulation in rat neocortex: a parametric study using multielectrode recordings. J. Neurophysiol. 90:53024–39
     [Google Scholar]
  118. 118.
    Histed MH, Bonin V, Reid RC. 2009. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63:4508–22
     [Google Scholar]
  119. 119.
    Kumaravelu K, Sombeck J, Miller LE, Bensmaia SJ, Grill WM. 2022. Stoney vs. Histed: quantifying the spatial effects of intracortical microstimulation. Brain Stimul. 15:1141–51
     [Google Scholar]
  120. 120.
    Dadarlat MC, Sun Y, Stryker MP. 2019. Widespread activation of awake mouse cortex by electrical stimulation. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)1113–17. Piscataway, NJ: IEEE
     [Google Scholar]
  121. 121.
    Stieger KC, Eles JR, Ludwig KA, Kozai TDY. 2022. Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation. J. Neural Eng. 19:026024
     [Google Scholar]
  122. 122.
    Eles JR, Stieger KC, Kozai TD. 2021. The temporal pattern of intracortical microstimulation pulses elicits distinct temporal and spatial recruitment of cortical neuropil and neurons. J. Neural Eng. 18:015001
     [Google Scholar]
  123. 123.
    Stieger KC, Eles JR, Ludwig KA, Kozai TD. 2020. In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons of male mice. J. Neurosci. Res. 98:102072–95
     [Google Scholar]
  124. 124.
    Venkatraman S, Carmena JM. 2009. Behavioral modulation of stimulus-evoked oscillations in barrel cortex of alert rats. Front. Integr. Neurosci. 3:10
     [Google Scholar]
  125. 125.
    Trevathan JK, Asp AJ, Nicolai EN, Trevathan JM, Kremer NA et al. 2021. Calcium imaging in freely moving mice during electrical stimulation of deep brain structures. J. Neural Eng. 18:026008
     [Google Scholar]
  126. 126.
    Kumaravelu K, Tomlinson T, Callier T, Sombeck J, Bensmaia SJ et al. 2020. A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation. J. Neural Eng. 17:046045
     [Google Scholar]
  127. 127.
    Bashivan P, Kar K, DiCarlo JJ. 2019. Neural population control via deep image synthesis. Science 364:6439eaav9436
     [Google Scholar]
  128. 128.
    Romo R, Hernández A, Zainos A, Salinas E. 1998. Somatosensory discrimination based on cortical microstimulation. Nature 292:387–90
     [Google Scholar]
  129. 129.
    Leal-Campanario R, Delgado-García JM, Gruart A. 2006. Microstimulation of the somatosensory cortex can substitute for vibrissa stimulation during Pavlovian conditioning. PNAS 103:2610052–57
     [Google Scholar]
  130. 130.
    Otto KJ, Rousche PJ, Kipke DR. 2005. Microstimulation in auditory cortex provides a substrate for detailed behaviors. Hear. Res. 210:1/2112–17
     [Google Scholar]
  131. 131.
    Otto KJ, Rousche PJ, Kipke DR. 2005. Cortical microstimulation in auditory cortex of rat elicits best-frequency dependent behaviors. J. Neural Eng. 2:242–51
     [Google Scholar]
  132. 132.
    Callier T, Brantly NW, Caravelli A, Bensmaia SJ. 2020. The frequency of cortical microstimulation shapes artificial touch. PNAS 117:21191–200
     [Google Scholar]
  133. 133.
    Butovas S, Schwarz C. 2007. Detection psychophysics of intracortical microstimulation in rat primary somatosensory cortex. Eur. J. Neurosci. 25:72161–69
     [Google Scholar]
  134. 134.
    Callier T, Schluter EW, Tabot GA, Miller LE, Tenore FV, Bensmaia SJ. 2015. Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex. J. Neural Eng. 12:056010
     [Google Scholar]
  135. 135.
    Fitzsimmons NA, Drake W, Hanson TL, Lebedev MA, Nicolelis MAL. 2007. Primate reaching cued by multichannel spatiotemporal cortical microstimulation. J. Neurosci. 27:215593–602
     [Google Scholar]
  136. 136.
    Doherty JEO, Lebedev MA, Li Z, Nicolelis MAL 2012. Virtual active touch using randomly patterned intracortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 20:185–93
     [Google Scholar]
  137. 137.
    Thomson EE, Carra R, Nicolelis MA. 2013. Perceiving invisible light through a somatosensory cortical prosthesis. Nat. Commun. 4:1482
     [Google Scholar]
  138. 138.
    Richardson AG, Ghenbot Y, Liu X, Hao H, Rinehart C et al. 2019. Learning active sensing strategies using a sensory brain–machine interface. PNAS 116:3517509–14
     [Google Scholar]
  139. 139.
    Sober SJ, Sabes PN. 2003. Multisensory integration during motor planning. J. Neurosci. 23:186982–92
     [Google Scholar]
  140. 140.
    Ernst MO, Banks MS. 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:6870429–33
     [Google Scholar]
  141. 141.
    Dadarlat MC, Sabes PN. 2016. Encoding and decoding of multi-channel ICMS in macaque somatosensory cortex. IEEE Trans. Hapt. 9:4508–14
     [Google Scholar]
  142. 142.
    Risso G, Valle G, Iberite F, Strauss I, Stieglitz T et al. 2019. Optimal integration of intraneural somatosensory feedback with visual information: a single-case study. Sci. Rep. 9:7916
     [Google Scholar]
  143. 143.
    Makino H, Hwang EJ, Hedrick NG, Komiyama T. 2016. Circuit mechanisms of sensorimotor learning. Neuron 92:4705–21
     [Google Scholar]
  144. 144.
    Koralek AC, Jin X, Long JD II, Costa RM, Carmena JM 2012. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483:7389331–35
     [Google Scholar]
  145. 145.
    Long JD, Carmena JM. 2013. Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus. J. Neurophysiol. 109:102585–95
     [Google Scholar]
  146. 146.
    Recanzone GH, Merzenich MM, Dinse HR. 1992. Expansion of the cortical representation of a specific skin field in primary somatosensory cortex by intracortical microstimulation. Cereb. Cortex 2:3181–96
     [Google Scholar]
  147. 147.
    Dinse HR, Recanzone GH, Merzenich MM. 1993. Alterations in correlated activity parallel ICMS-induced representational plasticity. NeuroReport 5:2173–76
     [Google Scholar]
  148. 148.
    Fox K, Stryker M. 2017. Integrating Hebbian and homeostatic plasticity: introduction. Philos. Trans. R. Soc. B 372:171520160413
     [Google Scholar]
  149. 149.
    Jackson A, Mavoori J, Fetz EE. 2006. Long-term motor cortex plasticity induced by an electronic neural implant. Nature 444:711556–60
     [Google Scholar]
  150. 150.
    Rebesco JM, Stevenson IH, Körding KP, Solla SA, Miller LE. 2010. Rewiring neural interactions by micro-stimulation. Front. Syst. Neurosci. 4:39
     [Google Scholar]
  151. 151.
    Rebesco JM, Miller LE. 2011. Enhanced detection threshold for in vivo cortical stimulation produced by Hebbian conditioning. J. Neural Eng. 8:016011
     [Google Scholar]
  152. 152.
    Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse HR. 1992. Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency-discrimination task. J. Neurophysiol. 67:51031–56
     [Google Scholar]
  153. 153.
    Ni AM, Maunsell JHR. 2010. Microstimulation reveals limits in detecting different signals from a local cortical region. Curr. Biol. 20:9824–28
     [Google Scholar]
  154. 154.
    Dalgleish HW, Russell LE, Packer AM, Roth A, Gauld OM et al. 2020. How many neurons are sufficient for perception of cortical activity?. eLife 9:e58889
     [Google Scholar]
  155. 155.
    Houweling AR, Brecht M. 2008. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 451:717465–68
     [Google Scholar]
  156. 156.
    Jeon BB, Fuchs T, Chase SM, Kuhlman SJ. 2022. Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task. Nat. Commun. 13:3638
     [Google Scholar]
  157. 157.
    Salzman DC, Britten KH, Newsome WT. 1990. Cortical microstimulation influences judgements of motion direction. Nature 346:174–77
     [Google Scholar]
  158. 158.
    Romo R, Hernández A, Zainos A, Brody CD, Lemus L. 2000. Sensing without touching: psychophysical performance based on cortical microstimulation. Neuron 26:1273–78
     [Google Scholar]
  159. 159.
    Hughes CL, Flesher SN, Weiss JM, Boninger M, Collinger JL, Gaunt RA 2021. Perception of microstimulation frequency in human somatosensory cortex. eLife 10:e65128
     [Google Scholar]
  160. 160.
    Salas MA, Bashford L, Kellis S, Jafari M, Jo H et al. 2018. Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation. eLife 7:e32904
     [Google Scholar]
  161. 161.
    Heming E, Sanden A, Kiss ZHT. 2010. Designing a somatosensory neural prosthesis: percepts evoked by different patterns of thalamic stimulation. J. Neural Eng. 7:064001
     [Google Scholar]
  162. 162.
    D'Anna E, Petrini FM, Artoni F, Popovic I, Simanić I et al. 2017. A somatotopic bidirectional hand prosthesis with transcutaneous electrical nerve stimulation based sensory feedback. Sci. Rep. 7:10930
     [Google Scholar]
/content/journals/10.1146/annurev-bioeng-110220-110833
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Neural Plasticity in Sensorimotor Brain–Machine Interfaces
Annual Review of Biomedical Engineering 25, 51 (2023); https://doi.org/10.1146/annurev-bioeng-110220-110833
/content/journals/10.1146/annurev-bioeng-110220-110833
/content/journals/10.1146/annurev-bioeng-110220-110833
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