1932

Abstract

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-092619-094115
2020-07-08
2024-04-17
Loading full text...

Full text loading...

/deliver/fulltext/neuro/43/1/annurev-neuro-092619-094115.html?itemId=/content/journals/10.1146/annurev-neuro-092619-094115&mimeType=html&fmt=ahah

Literature Cited

  1. Adamantidis A, Arber S, Bains JS, Bamberg E, Bonci A et al. 2015. Optogenetics: 10 years after ChR2 in neurons—views from the community. Nat. Neurosci. 18:1202–12
    [Google Scholar]
  2. Afshar A, Santhanam G, Yu BM, Ryu SI, Sahani M, Shenoy KV 2011. Single-trial neural correlates of arm movement preparation. Neuron 71:3555–64
    [Google Scholar]
  3. Ahrens MB, Li JM, Orger MB, Robson DN, Schier AF et al. 2012. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485:7399471–77
    [Google Scholar]
  4. Allen WE, Chen MZ, Pichamoorthy N, Tien RH, Pachitariu M et al. 2019. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364:6437eaav3932
    [Google Scholar]
  5. Ames KC, Churchland MM 2019. Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response. eLife 8:e46159
    [Google Scholar]
  6. Ames KC, Ryu SI, Shenoy KV 2014. Neural dynamics of reaching following incorrect or absent motor preparation. Neuron 81:2438–51
    [Google Scholar]
  7. Ames KC, Ryu SI, Shenoy KV 2019. Simultaneous motor preparation and execution in a last-moment reach correction task. Nat. Commun. 10:12718
    [Google Scholar]
  8. Andalman AS, Burns VM, Lovett-Barron M, Broxton M, Poole B et al. 2019. Neuronal dynamics regulating brain and behavioral state transitions. Cell 177:4970–85.e20
    [Google Scholar]
  9. Athlaye VR, Ganguly K, Costa RM, Carmena JM 2017. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron 93:4955–970.e5
    [Google Scholar]
  10. Barak O. 2017. Recurrent neural networks as versatile tools of neuroscience research. Curr. Opin. Neurobiol. 46:1–6
    [Google Scholar]
  11. Bashivan P, Kar K, DiCarlo JJ 2019. Neural population control via deep image synthesis. Science 364:6439eaav9436
    [Google Scholar]
  12. Bastian A, Riehle A, Erlhagen W, Schöner G 1998. Prior information preshapes the population representation of movement direction in motor cortex. Neuroreport 9:2315–19
    [Google Scholar]
  13. Bastian A, Schöner G, Riehle A 2003. Preshaping and continuous evolution of motor cortical representations during movement preparation. Eur. J. Neurosci. 18:72047–58
    [Google Scholar]
  14. Briggman KL, Abarbanel HDI, Kristan WB 2005. Optical imaging of neuronal populations during decision-making. Science 307:5711896–901
    [Google Scholar]
  15. Briggman KL, Abarbanel HDI, Kristan WB 2006. From crawling to cognition: analyzing the dynamical interactions among populations of neurons. Curr. Opin. Neurobiol. 16:2135–44
    [Google Scholar]
  16. Broome BM, Jayaraman V, Laurent G 2006. Encoding and decoding of overlapping odor sequences. Neuron 51:4467–82
    [Google Scholar]
  17. Brunton BW, Johnson LA, Ojemann JG, Kutz JN 2016. Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. J. Neurosci. Methods 258:1–15
    [Google Scholar]
  18. Buonomano DV 2000. Decoding temporal information: a model based on short-term synaptic plasticity. J. Neurosci. 20:31129–41
    [Google Scholar]
  19. Buonomano DV, Maass W 2009. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10:2113–25
    [Google Scholar]
  20. Carrillo-Reid L, Han S, Yang W, Akrouh A, Yuste R 2019. Controlling visually guided behavior by holographic recalling of cortical ensembles. Cell 178:2447–57.e5
    [Google Scholar]
  21. Chaisangmongkon W, Swaminathan SK, Freedman DJ, Wang XJ 2017. Computing by robust transience: how the fronto-parietal network performs sequential, category-based decisions. Neuron 93:61504–17.e4
    [Google Scholar]
  22. 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]
  23. Chaudhuri R, Fiete I 2016. Computational principles of memory. Nat. Neurosci. 19:3394–403
    [Google Scholar]
  24. Chaudhuri R, Gerçek B, Pandey B, Peyrache A, Fiete I 2019. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat. Neurosci. 22:91512–20
    [Google Scholar]
  25. Cherian A, Ernandes HL, Miller LE 2013. Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics. J. Neurophysiol. 110:3768–83
    [Google Scholar]
  26. Chestek CA, Batista AP, Santhanam G, Yu BM, Afshar A et al. 2007. Single-neuron stability during repeated reaching in macaque premotor cortex. J. Neurosci. 27:10742–50
    [Google Scholar]
  27. Chettih SN, Harvey CD 2019. Single-neuron perturbations reveal feature-specific competition in V1. Nature 567:7748334–40
    [Google Scholar]
  28. Churchland MM, Afshar A, Shenoy KV 2006a. A central source of movement variability. Neuron 52:61085–96
    [Google Scholar]
  29. Churchland MM, Cunningham JP, Kaufman MT, Foster JD, Nuyujukian P et al. 2012. Neural population dynamics during reaching. Nature 487:740551–56
    [Google Scholar]
  30. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV 2010. Cortical preparatory activity: representation of movement or first cog in a dynamical machine. ? Neuron 68:3387–400
    [Google Scholar]
  31. Churchland MM, Santhanam G, Shenoy KV 2006b. Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. J. Neurophysiol. 96:63130–46
    [Google Scholar]
  32. Churchland MM, Shenoy KV 2007a. Delay of movement caused by disruption of cortical preparatory activity. J. Neurophysiol. 97:1348–59
    [Google Scholar]
  33. Churchland MM, Shenoy KV 2007b. Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. J. Neurophysiol. 97:64235–57
    [Google Scholar]
  34. Churchland MM, Yu BM, Ryu SI, Santhanam G, Shenoy KV 2006c. Neural variability in premotor cortex provides a signature of motor preparation. J. Neurosci. 26:143697–712
    [Google Scholar]
  35. Cisek P 2006. Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26:389761–70
    [Google Scholar]
  36. Cisek P, Crammond DJ, Kalaska JF 2003. Neural activity in primary motor and dorsal premotor cortex in reaching tasks with the contralateral versus ipsilateral arm. J. Neurophysiol. 89:2922–42
    [Google Scholar]
  37. Cohen MR, Maunsell JHR 2009. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12:121594–600
    [Google Scholar]
  38. Cunningham JP, Yu BM 2014. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17:111500–9
    [Google Scholar]
  39. Druckmann S, Chklovskii DB 2012. Neuronal circuits underlying persistent representations despite time varying activity. Curr. Biol. 22:222095–103
    [Google Scholar]
  40. Egger SW, Remington ED, Chang C-J, Jazayeri M 2019. Internal models of sensorimotor integration regulate cortical dynamics. Nat. Neurosci. 22:111871–82
    [Google Scholar]
  41. Elsayed G, Cunningham J 2017. Structure in neural population recordings: an expected byproduct of simpler phenomena?. Nat. Neurosci. 20:1310–18
    [Google Scholar]
  42. Elsayed GF, Lara AH, Kaufman MT, Churchland MM, Cunningham JP 2016. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7:13239
    [Google Scholar]
  43. Evarts EV 1964. Temporal patterns of discharge of pyramidal tract neurons during sleep and waking in the monkey. J. Neurophysiol. 27:152–71
    [Google Scholar]
  44. Evarts EV 1968. Relation of pyramidal tract activity to force exerted during voluntary movement. J. Neurophysiol. 31:114–27
    [Google Scholar]
  45. Even-Chen N, Sheffer B, Vyas S, Ryu SI, Shenoy KV 2019. Structure and variability of delay activity in premotor cortex. PLOS Comput. Biol. 15:2e1006808
    [Google Scholar]
  46. Flint RD, Scheid MR, Wright ZA, Solla SA, Slutzky MW 2016. Long-term stability of motor cortical activity: implications for brain machine interfaces and optimal feedback control. J. Neurosci. 36:3623–32
    [Google Scholar]
  47. Freedman DJ, Assad JA 2006. Experience-dependent representation of visual categories in parietal cortex. Nature 443:85–88
    [Google Scholar]
  48. Gallego JA, Perich MG, Chowdhury RH, Solla SA, Miller LE 2020. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23:260–70
    [Google Scholar]
  49. Gallego JA, Perich MG, Miller LE, Solla SA 2017. Neural manifolds for the control of movement. Neuron 94:5978–84
    [Google Scholar]
  50. Gallego JA, Perich MG, Naufel SN, Ethier C, Solla SA, Miller LE 2018. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nat. Commun. 9:4233
    [Google Scholar]
  51. Georgopoulos A, Kalaska J, Caminiti R, Massey J 1982. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neurosci. 2:111527–37
    [Google Scholar]
  52. Georgopoulos AP, Crutcher MD, Schwartz AB 1989. Cognitive spatial-motor processes. 3. Motor cortical prediction of movement direction during an instructed delay period. Exp. Brain Res. 75:1183–94
    [Google Scholar]
  53. Georgopoulos AP, Schwartz AB, Kettner RE 1986. Neuronal population coding of movement direction. Science 233:47711416–19
    [Google Scholar]
  54. Golub M, Sussillo D 2018. FixedPointFinder: a Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks. J. Open Source Softw. 3:311003
    [Google Scholar]
  55. Golub MD, Chase SM, Batista AP, Yu BM 2016. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr. Opin. Neurobiol. 37:53–58
    [Google Scholar]
  56. Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI et al. 2018. Learning by neural reassociation. Nat. Neurosci. 21:607–16
    [Google Scholar]
  57. Hall TM, deCarvalho F, Jackson A 2014. A common structure underlies low-frequency cortical dynamics in movement, sleep, and sedation. Neuron 83:51185–99
    [Google Scholar]
  58. Hennequin G, Vogels TP, Gerstner W 2014. Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron 82:61394–406
    [Google Scholar]
  59. Hennig JA, Golub MD, Lund PJ, Sadtler PT, Oby ER et al. 2018. Constraints on neural redundancy. eLife 7:e36774
    [Google Scholar]
  60. Hodgkin AL, Huxley AF 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:4500–44
    [Google Scholar]
  61. Hubel DH, Wiesel TN 1962. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160:1106–54
    [Google Scholar]
  62. Inagaki HK, Fontolan L, Romani S, Svoboda K 2019. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 566:7743212–17
    [Google Scholar]
  63. 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]
  64. Jazayeri M, Afraz A 2017. Navigating the neural space in search of the neural code. Neuron 93:51003–14
    [Google Scholar]
  65. Jazayeri M, Shadlen MN 2015. A neural mechanism for sensing and reproducing a time interval. Curr. Biol. 25:202599–609
    [Google Scholar]
  66. Jun JJ, Steinmetz NA, Siegle JH, Denman DJ, Bauza M et al. 2017. Fully integrated silicon probes for high-density recording of neural activity. Nature 551:232–36
    [Google Scholar]
  67. Kalaska JF 2019. Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates [version 1; peer review: 4 approved]. F1000Research 8:749
    [Google Scholar]
  68. Karmarkar UR, Buonomano DV 2007. Timing in the absence of clocks: encoding time in neural network states. Neuron 53:3427–38
    [Google Scholar]
  69. Kato S, Kaplan HS, Schrödel T, Skora S, Lindsay TH et al. 2015. Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163:3656–69
    [Google Scholar]
  70. Kaufman MT, Churchland MM, Ryu SI, Shenoy KV 2014. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17:440–48
    [Google Scholar]
  71. Kaufman MT, Churchland MM, Santhanam G, Yu BM, Afshar A et al. 2010. Roles of monkey premotor neuron classes in movement preparation and execution. J. Neurophysiol. 104:2799–810
    [Google Scholar]
  72. Kaufman MT, Churchland MM, Shenoy KV 2013. The roles of monkey M1 neuron classes in movement preparation and execution. J. Neurophysiol. 110:4817–25
    [Google Scholar]
  73. Kaufman MT, Seely JS, Sussillo D, Ryu SI, Shenoy KV, Churchland MM 2016. The largest response component in the motor cortex reflects movement timing but not movement type. eNeuro 3:4ENEURO.0085–16.2016
    [Google Scholar]
  74. Killeen PR, Fetterman JG 1988. A behavioral theory of timing. Psychol. Rev. 95:2274–95
    [Google Scholar]
  75. Kim SS, Rouault H, Druckmann S, Jayaraman V 2017. Ring attractor dynamics in the Drosophila central brain. Science 356:6340849–53
    [Google Scholar]
  76. Kobak D, Brendel W, Constantinidis C, Feierstein CE, Kepecs A et al. 2016. Demixed principal component analysis of neural population data. eLife 5:e10989
    [Google Scholar]
  77. Krakauer JW, Hadjiosif AM, Xu J, Wong AL, Haith AM 2019. Motor learning. Compr. Physiol. 9:2613–63
    [Google Scholar]
  78. Lara AH, Cunningham JP, Churchland MM 2018a. Different population dynamics in the supplementary motor area and motor cortex during reaching. Nat. Commun. 9:2754
    [Google Scholar]
  79. Lara AH, Elsayed GF, Zimnik AJ, Cunningham JP, Churchland MM 2018b. Conservation of preparatory neural events in monkey motor cortex regardless of how movement is initiated. eLife 7:e31826
    [Google Scholar]
  80. Lebedev MA, Ossadtchi A, Mill NA, Urpi NA, Cervera MR, Nicolelis MAL 2019. Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics. Sci. Rep. 9:18978
    [Google Scholar]
  81. Li N, Daie K, Svoboda K, Druckmann S 2016. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532:459–64
    [Google Scholar]
  82. Linderman SW, Johnson MJ, Miller AC, Adams RP, Blei DM, Paninski L 2017. Bayesian learning and inference in recurrent switching linear dynamical systems. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 54914–922 Brookline, MA: Microtome Publ.
    [Google Scholar]
  83. Luenberger DG 1979. Introduction to Dynamic Systems: Theory, Models, and Applications Hoboken, NJ: John Wiley & Sons
  84. Maass W, Natschläger T, Markram H 2002. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14:112531–60
    [Google Scholar]
  85. Mante V, Sussillo D, Shenoy KV, Newsome WT 2013. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503:78–84
    [Google Scholar]
  86. Marshel JH, Kim YS, Machado TA, Quirin S, Benson B et al. 2019. Cortical layer–specific critical dynamics triggering perception. Science 365:6453eaaw5202
    [Google Scholar]
  87. Mauk MD, Buonomano DV 2004. The neural basis of temporal processing. Annu. Rev. Neurosci. 27:307–40
    [Google Scholar]
  88. Mazor O, Laurent G 2005. Transient dynamics versus fixed points in odor representations by locust antennal lobe projection neurons. Neuron 48:4661–73
    [Google Scholar]
  89. Meck WH 1996. Neuropharmacology of timing and time perception. Cogn. Brain Res. 3:3–4227–42
    [Google Scholar]
  90. Messier J, Kalaska JF 1997. Differential effect of task conditions on errors of direction and extent of reaching movements. Exp. Brain Res. 115:469–78
    [Google Scholar]
  91. Messier J, Kalaska JF 1999. Comparison of variability of initial kinematics and endpoints of reaching movements. Exp. Brain Res. 125:2139–52
    [Google Scholar]
  92. Michaels JA, Dann B, Intveld RW, Scherberger H 2015. Predicting reaction time from the neural state space of the premotor and parietal grasping network. J. Neurosci. 35:3211415–32
    [Google Scholar]
  93. Michaels JA, Dann B, Scherberger H 2016. Neural population dynamics during reaching are better explained by a dynamical system than representational tuning. PLOS Comput. Biol. 12:11e1005175
    [Google Scholar]
  94. Miller EK, Lundqvist M, Bastos AM 2018. Working memory 2.0. Neuron 100:2463–75
    [Google Scholar]
  95. Mitchell JF, Sundberg KA, Reynolds JH 2009. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63:6879–88
    [Google Scholar]
  96. Murakami M, Mainen ZF 2015. Preparing and selecting actions with neural populations: toward cortical circuit mechanisms. Curr. Opin. Neurobiol. 33:40–46
    [Google Scholar]
  97. Naumann EA, Fitzgerald JE, Dunn TW, Rihel J, Sompolinsky H, Engert F 2016. From whole-brain data to functional circuit models: the zebrafish optomotor response. Cell 167:4947–60.e20
    [Google Scholar]
  98. Nichols ALA, Eichler T, Latham R, Zimmer M 2017. A global brain state underlies C. elegans sleep behavior. Science 356:6344eaam6851
    [Google Scholar]
  99. 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]
  100. Orsborn AL, Moorman HG, Overduin SA, Shanechi MM, Dimitrov DF, Carmena JM 2014. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82:61380–93
    [Google Scholar]
  101. O'Shea DJ, Trautmann E, Chandrasekaran C, Stavisky S, Kao JC et al. 2017. The need for calcium imaging in nonhuman primates: new motor neuroscience and brain-machine interfaces. Exp. Neurol. 287:Pt. 4437–51
    [Google Scholar]
  102. Otchy TM, Wolff SBE, Rhee JY, Pehlevan C, Kawai R et al. 2015. Acute off-target effects of neural circuit manipulations. Nature 528:7582358–63
    [Google Scholar]
  103. Pandarinath C, Ames KC, Russo AA, Farshchian A, Miller LE et al. 2018a. Latent factors and dynamics in motor cortex and their application to brain–machine interfaces. J. Neurosci. 38:449390–401
    [Google Scholar]
  104. Pandarinath C, Gilja V, Blabe CH, Nuyujukian P, Sarma AA et al. 2015. Neural population dynamics in human motor cortex during movements in people with ALS. eLife 4:e07436
    [Google Scholar]
  105. Pandarinath C, O'Shea DJ, Collins J, Jozefowicz R, Stavisky SD et al. 2018b. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15:10805–15
    [Google Scholar]
  106. Paninski L, Cunningham JP 2018. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr. Opin. Neurobiol. 50:232–41
    [Google Scholar]
  107. Perfiliev S, Isa T, Johnels B, Steg G, Wessberg J 2010. Reflexive limb selection and control of reach direction to moving targets in cats, monkeys, and humans. J. Neurophysiol. 104:52423–32
    [Google Scholar]
  108. Perich MG, Gallego JA, Miller LE 2018. A neural population mechanism for rapid learning. Neuron 100:4964–76.e7
    [Google Scholar]
  109. Petreska B, Yu BM, Cunningham JP, Santhanam G, Ryu SI et al. 2011. Dynamical segmentation of single trials from population neural data. Advances in Neural Information Processing Systems 24 J Shawe-Taylor, RS Zemel, PL Bartlett, F Pereira, KQ Weinberger San Diego, CA: NeurIPS
    [Google Scholar]
  110. Portugues R, Feierstein CE, Engert F, Orger MB 2014. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81:61328–43
    [Google Scholar]
  111. Rabinovich M, Huerta R, Laurent G 2008. Transient dynamics for neural processing. Science 321:588548–50
    [Google Scholar]
  112. Rabinovich M, Volkovskii A, Lecanda P, Huerta R, Abarbanel HDI, Laurent G 2001. Dynamical encoding by networks of competing neuron groups: winnerless competition. Phys. Rev. Lett. 87:6068102
    [Google Scholar]
  113. Raposo D, Kaufman MT, Churchland AK 2014. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17:121784–92
    [Google Scholar]
  114. Remington ED, Egger SW, Narain D, Wang J, Jazayeri M 2018a. Time in the brain a dynamical systems perspective on flexible motor timing. Trends Cogn. Sci. 22:10938–52
    [Google Scholar]
  115. Remington ED, Narain D, Hosseini EA, Jazayeri M 2018b. Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics. Neuron 98:51005–19.e5
    [Google Scholar]
  116. Riehle A, Requin J 1989. Monkey primary motor and premotor cortex: single-cell activity related to prior information about direction and extent of an intended movement. J. Neurophysiol. 61:3534–49
    [Google Scholar]
  117. Romo R, Schultz W 1992. Role of primate basal ganglia and frontal cortex in the internal generation of movements. III. Neuronal activity in the supplementary motor area. Exp. Brain Res. 91:3396–407
    [Google Scholar]
  118. Rosenbaum DA 1980. Human movement initiation: specification of arm, direction, and extent. J. Exp. Psychol. Gen. 109:4444–74
    [Google Scholar]
  119. Rouse AG, Schieber MH 2018. Condition-dependent neural dimensions progressively shift during reach to grasp. Cell Rep 25:113158–68.e3
    [Google Scholar]
  120. Russo AA, Bittner SR, Perkins SM, Seely JS, London BM et al. 2018. Motor cortex embeds muscle-like commands in an untangled population response. Neuron 97:4953–66.e8
    [Google Scholar]
  121. Russo AA, Khajeh R, Bittner SR, Perkins SM, Cunningham JP et al. 2019. Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation. bioRxiv 650002. https://doi.org/10.1101/650002
    [Crossref]
  122. Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI et al. 2014. Neural constraints on learning. Nature 512:7515423–26
    [Google Scholar]
  123. 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]
  124. Santhanam G, Yu BM, Gilja V, Ryu SI, Afshar A et al. 2009. Factor-analysis methods for higher-performance neural prostheses. J. Neurophysiol. 102:21315–30
    [Google Scholar]
  125. Sauerbrei BA, Guo J, Cohen JD, Mischiati M, Guo W et al. 2020. Cortical pattern generation during dexterous movement is input-driven. Nature 577:386–91
    [Google Scholar]
  126. Saxena S, Cunningham JP 2019. Towards the neural population doctrine. Curr. Opin. Neurobiol. 55:103–11
    [Google Scholar]
  127. Seely JS, Kaufman MT, Ryu SI, Shenoy KV, Cunningham JP, Churchland MM 2016. Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1. PLOS Comput. Biol. 12:11e1005164
    [Google Scholar]
  128. Semedo JD, Zandvakili A, Machens CK, Yu BM, Kohn A 2019. Cortical areas interact through a communication subspace. Neuron 102:1249–59.e4
    [Google Scholar]
  129. Sheahan HR, Franklin DW, Wolpert DM 2016. Motor planning, not execution, separates motor memories. Neuron 92:4773–79
    [Google Scholar]
  130. Shenoy KV, Carmena JM 2014. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84:4665–80
    [Google Scholar]
  131. Shenoy KV, Sahani M, Churchland MM 2013. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36:337–59
    [Google Scholar]
  132. Sohn H, Narain D, Meirhaeghe N, Jazayeri M 2019. Bayesian computation through cortical latent dynamics. Neuron 103:5934–47.e5
    [Google Scholar]
  133. Song HF, Yang GR, Wang XJ 2016. Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework. PLOS Comput. Biol. 12:2e1004792
    [Google Scholar]
  134. Song HF, Yang GR, Wang XJ 2017. Reward-based training of recurrent neural networks for cognitive and value-based tasks. eLife 6:e21492
    [Google Scholar]
  135. Stavisky SD, Kao JC, Ryu SI, Shenoy KV 2017a. Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions. Neuron 95:1195–208.e9
    [Google Scholar]
  136. Stavisky SD, Kao JC, Ryu SI, Shenoy KV 2017b. Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model. J. Neurosci. 37:71721–32
    [Google Scholar]
  137. Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P et al. 2019. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 8:e46015
    [Google Scholar]
  138. Stopfer M, Jayaraman V, Laurent G 2003. Intensity versus identity coding in an olfactory system. Neuron 39:6991–1004
    [Google Scholar]
  139. Stringer C, Pachitariu M, Steinmetz N, Reddy CB, Carandini M, Harris KD 2019. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364:6437eaav7893
    [Google Scholar]
  140. Sussillo D, Abbott LF 2009. Generating coherent patterns of activity from chaotic neural networks. Neuron 63:4544–57
    [Google Scholar]
  141. Sussillo D, Barak O 2013. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput 25:3626–49
    [Google Scholar]
  142. Sussillo D, Churchland MM, Kaufman MT, Shenoy KV 2015. A neural network that finds a naturalistic solution for the production of muscle activity. Nat. Neurosci. 18:1025–33
    [Google Scholar]
  143. Swaminathan SK, Freedman DJ 2012. Preferential encoding of visual categories in parietal cortex compared with prefrontal cortex. Nat. Neurosci. 15:2315–20
    [Google Scholar]
  144. Tanji J, Evarts EV 1976. Anticipatory activity of motor cortex neurons in relation to direction of an intended movement. J. Neurophysiol. 39:51062–68
    [Google Scholar]
  145. Thoroughman KA, Shadmehr R 1999. Electromyographic correlates of learning an internal model of reaching movements. J. Neurosci. 19:198573–88
    [Google Scholar]
  146. 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]
  147. Treisman M 1963. Temporal discrimination and the indifference interval. Implications for a model of the “internal clock. .” Psychol. Monogr. 77:131–31
    [Google Scholar]
  148. Vyas S, Even-Chen N, Stavisky SD, Ryu SI, Nuyujukian P, Shenoy KV 2018. Neural population dynamics underlying motor learning transfer. Neuron 97:51177–86.e3
    [Google Scholar]
  149. Vyas S, O'Shea DJ, Ryu SI, Shenoy KV 2020. Causal role of motor preparation during error-driven learning. Neuron 106:2329–39
    [Google Scholar]
  150. Wang J, Narain D, Hosseini EA, Jazayeri M 2018. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 21:1102–10
    [Google Scholar]
  151. Wärnberg E, Kumar A 2019. Perturbing low dimensional activity manifolds in spiking neuronal networks. PLOS Comput. Biol. 15:5e1007074
    [Google Scholar]
  152. Wei K, Körding K 2009. Relevance of error: What drives motor adaptation?. J. Neurophysiol. 101:2655–64
    [Google Scholar]
  153. Williams AH, Kim TH, Wang F, Vyas S, Ryu SI et al. 2018. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98:61099–115.e8
    [Google Scholar]
  154. Williamson RC, Doiron B, Smith MA, Yu BM 2019. Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. Curr. Opin. Neurobiol. 55:40–47
    [Google Scholar]
  155. Wise S 1985. The primate premotor cortex: past, present, and preparatory. Annu. Rev. Neurosci. 8:1–19
    [Google Scholar]
  156. Wong AL, Haith AM, Krakauer JW 2015. Motor planning. Neuroscientist 21:4385–98
    [Google Scholar]
  157. Yang GR, Joglekar MR, Song HF, Newsome WT, Wang XJ 2019. Task representations in neural networks trained to perform many cognitive tasks. Nat. Neurosci. 22:2297–306
    [Google Scholar]
  158. Yang W, Carrillo-Reid L, Bando Y, Peterka DS, Yuste R 2018. Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions. eLife 7:e32671
    [Google Scholar]
  159. Yu BM, Afshar A, Santhanam G, Ryu SI, Shenoy KV, Sahani M 2005. Extracting dynamical structure embedded in neural activity. Advances in Neural Information Processing Systems 18 Y Weiss, B Schölkopf, JC Platt San Diego, CA: NeurIPS
    [Google Scholar]
  160. Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M 2009. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102:1614–35
    [Google Scholar]
  161. 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]
/content/journals/10.1146/annurev-neuro-092619-094115
Loading
/content/journals/10.1146/annurev-neuro-092619-094115
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error