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Annual Review of Neuroscience

Volume 41, 2018

Cognition as a Window into Neuronal Population Space

Abstract

Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.

Keyword(s): attention, cognition, information coding, noise correlations, spike count correlations, visual cortex
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2018-07-08
2025-04-26
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Literature Cited

  1. Abbott LF, Dayan P 1999. The effect of correlated variability on the accuracy of a population code. Neural Comput 11:91–101
     [Google Scholar]
  2. Albright TD, Desimone R 1987. Local precision of visuotopic organization in the middle temporal area (MT) of the macaque. Exp. Brain Res. 65:582–92
     [Google Scholar]
  3. Anton-Erxleben K, Carrasco M 2013. Attentional enhancement of spatial resolution: linking behavioural and neurophysiological evidence. Nat. Rev. Neurosci. 14:188–200
     [Google Scholar]
  4. Averbeck BB, Latham PE, Pouget A 2006. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7:358–66
     [Google Scholar]
  5. Bair W, Zohary E, Newsome WT 2001. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 21:1676–97
     [Google Scholar]
  6. Bichot NP, Rossi AF, Desimone R 2005. Parallel and serial neural mechanisms for visual search in macaque area V4. Science 308:529–34
     [Google Scholar]
  7. Bishop WE, Degenhart AD, Oby ER, Batista AP, Chase SM et al. 2017. Extracting stable representations of neural population state from unstable neural recordings. Cosyne Abstracts 2017 Salt Lake City: UT
     [Google Scholar]
  8. Born RT, Bradley DC 2005. Structure and function of visual area MT. Annu. Rev. Neurosci. 28:157–89
     [Google Scholar]
  9. Bosman CA, Schoffelen J-M, Brunet N, Oostenveld R, Bastos AM et al. 2012. Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron 75:875–88
     [Google Scholar]
  10. Boudreau CE, Williford TH, Maunsell JHR 2006. Effects of task difficulty and target likelihood in area V4 of macaque monkeys. J. Neurophysiol. 96:2377–87
     [Google Scholar]
  11. Briggs F, Mangun GR, Usrey WM 2013. Attention enhances synaptic efficacy and the signal-to-noise ratio in neural circuits. Nature 499:476–80
     [Google Scholar]
  12. Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA 1996. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13:87–100
     [Google Scholar]
  13. Britten KH, Shadlen MN, Newsome WT, Movshon JA 1992. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12:4745–65
     [Google Scholar]
  14. Brody CD, Hanks TD 2016. Neural underpinnings of the evidence accumulator. Curr. Opin. Neurobiol. 37:149–57
     [Google Scholar]
  15. Buschman T, Miller E 2007. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315:1860–62
     [Google Scholar]
  16. Cadieu CF, Hong H, Yamins DL, Pinto N, Ardila D et al. 2014. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLOS Comput. Biol. 10:e1003963
     [Google Scholar]
  17. Carandini M, Churchland AK 2013. Probing perceptual decisions in rodents. Nat. Neurosci. 16:824–31
     [Google Scholar]
  18. Chandrasekaran C 2017. Computational principles and models of multisensory integration. Curr. Opin. Neurobiol. 43:25–34
     [Google Scholar]
  19. Chowdhury SA, DeAngelis GC 2008. Fine discrimination training alters the causal contribution of macaque area MT to depth perception. Neuron 60:367–77
     [Google Scholar]
  20. Churchland MM, Cunningham JP, Kaufman MT, Foster JD, Nuyujukian P et al. 2012. Neural population dynamics during reaching. Nature 487:51–56
     [Google Scholar]
  21. Clery S, Cumming BG, Nienborg H 2017. Decision-related activity in macaque V2 for fine disparity discrimination is not compatible with optimal linear readout. J. Neurosci. 37:715–25
     [Google Scholar]
  22. Cohen MR, Kohn A 2011. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14:811–19
     [Google Scholar]
  23. Cohen MR, Maunsell JHR 2009. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12:1594–600
     [Google Scholar]
  24. Cohen MR, Maunsell JHR 2011. Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron 70:1192–204
     [Google Scholar]
  25. Cohen MR, Newsome WT 2008. Context-dependent changes in functional circuitry in visual area MT. Neuron 60:162–73
     [Google Scholar]
  26. Cohen MR, Newsome WT 2009. Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J. Neurosci. 29:6635–48
     [Google Scholar]
  27. Cumming BG, Nienborg H 2016. Feedforward and feedback sources of choice probability in neural population responses. Curr. Opin. Neurobiol. 37:126–32
     [Google Scholar]
  28. Cunningham JP, Yu BM 2014. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17:1500–9
     [Google Scholar]
  29. da Silveira RA, Berry MJ 2014. High-fidelity coding with correlated neurons. PLOS Comput. Biol. 10:e1003970
     [Google Scholar]
  30. Dagnino B, Gariel-Mathis M-A, Roelfsema PR 2015. Microstimulation of area V4 has little effect on spatial attention and on perception of phosphenes evoked in area V1. J. Neurophysiol. 113:730–39
     [Google Scholar]
  31. DeAngelis GC, Uka T 2003. Coding of horizontal disparity and velocity by MT neurons in the alert macaque. J. Neurophysiol. 89:1094–111
     [Google Scholar]
  32. Desimone R, Duncan J 1995. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18:193–222
     [Google Scholar]
  33. DiCarlo JJ, Cox DD 2007. Untangling invariant object recognition. Trends Cogn. Sci. 11:333–41
     [Google Scholar]
  34. DiCarlo JJ, Zoccolan D, Rust NC 2012. How does the brain solve visual object recognition. Neuron 73:415–34
     [Google Scholar]
  35. Drugowitsch J, DeAngelis GC, Klier EM, Angelaki DE, Pouget A 2014. Optimal multisensory decision-making in a reaction-time task. eLife 3:e03005
     [Google Scholar]
  36. Ecker AS, Denfield GH, Bethge M, Tolias AS 2016. On the structure of neuronal population activity under fluctuations in attentional state. J. Neurosci. 36:1775–89
     [Google Scholar]
  37. Elsayed GF, Cunningham JP 2017. Structure in neural population recordings: an expected byproduct of simpler phenomena?. Nat. Neurosci. 20:1310–18
     [Google Scholar]
  38. 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]
  39. Ernst M, Banks M 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429–33
     [Google Scholar]
  40. Fetsch CR, Pouget A, DeAngelis GC, Angelaki DE 2011. Neural correlates of reliability-based cue weighting during multisensory integration. Nat. Neurosci. 15:146–54
     [Google Scholar]
  41. Fries P 2015. Rhythms for cognition: communication through coherence. Neuron 88:220–35
     [Google Scholar]
  42. Fries P, Reynolds JH, Rorie AE, Desimone R 2001. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–63
     [Google Scholar]
  43. Ganguly K, Dimitrov DF, Wallis JD, Carmena JM 2011. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14:662–67
     [Google Scholar]
  44. Gilbert CD, Sigman M 2007. Brain states: top-down influences in sensory processing. Neuron 54:677–96
     [Google Scholar]
  45. Gold JI, Shadlen MN 2007. The neural basis of decision making. Annu. Rev. Neurosci. 30:535–74
     [Google Scholar]
  46. 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]
  47. Goris RL, Movshon JA, Simoncelli EP 2014. Partitioning neuronal variability. Nat. Neurosci. 17:858–65
     [Google Scholar]
  48. Gregoriou GG, Gotts SJ, Zhou H, Desimone R 2009. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324:1207–10
     [Google Scholar]
  49. Gregoriou GG, Rossi AF, Ungerleider LG, Desimone R 2014. Lesions of prefrontal cortex reduce attentional modulation of neuronal responses and synchrony in V4. Nat. Neurosci. 17:1003–11
     [Google Scholar]
  50. Gu Y, Liu S, Fetsch CR, Yang Y, Fok S et al. 2011. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 71:750–61
     [Google Scholar]
  51. Heekeren HR, Marrett S, Ungerleider LG 2008. The neural systems that mediate human perceptual decision making. Nat. Rev. Neurosci. 9:467–79
     [Google Scholar]
  52. Herrero JL, Gieselmann M, Sanayei M, Thiele A 2013. Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors. Neuron 78:729–39
     [Google Scholar]
  53. Huang C, Ruff DA, Cohen MR, Doiron B 2017. Modeling within and across area neuronal variability in the visual system. Cosyne Abstracts 2017 Salt Lake City: UT
     [Google Scholar]
  54. Huang X, Lisberger SG 2009. Noise correlations in cortical area MT and their potential impact on trial-by-trial variation in the direction and speed of smooth-pursuit eye movements. J. Neurophysiol. 101:3012–30
     [Google Scholar]
  55. Jacobs R 1999. Optimal integration of texture and motion cues to depth. Vis. Res. 39:3621–29
     [Google Scholar]
  56. Jazayeri M, Afraz A 2017. Navigating the neural space in search of the neural code. Neuron 93:1003–14
     [Google Scholar]
  57. Jeanne JM, Sharpee TO, Gentner TQ 2013. Associative learning enhances population coding by inverting interneuronal correlation patterns. Neuron 78:352–63
     [Google Scholar]
  58. Kanashiro T, Ocker GK, Cohen MR, Doiron B 2017. Attentional modulation of neuronal variability in circuit models of cortex. eLife 6:e23978
     [Google Scholar]
  59. Kanitscheider I, Coen-Cagli R, Kohn A, Pouget A 2015a. Measuring Fisher information accurately in correlated neural populations. PLOS Comput. Biol. 11:e1004218
     [Google Scholar]
  60. Kanitscheider I, Coen-Cagli R, Pouget A 2015b. Origin of information-limiting noise correlations. PNAS 112:E6973–82
     [Google Scholar]
  61. 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]
  62. Khaligh-Razavi SM, Kriegeskorte N 2014. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLOS Comput. Biol. 10:e1003915
     [Google Scholar]
  63. Klink PC, Dagnino B, Gariel-Mathis MA, Roelfsema PR 2017. Distinct feedforward and feedback effects of microstimulation in visual cortex reveal neural mechanisms of texture segregation. Neuron 95:209–20.e3
     [Google Scholar]
  64. Knill DC 2007. Robust cue integration: a Bayesian model and evidence from cue-conflict studies with stereoscopic and figure cues to slant. J. Vis. 7(7):5
     [Google Scholar]
  65. Kohn A, Coen-Cagli R, Kanitscheider I, Pouget A 2016. Correlations and neuronal population information. Annu. Rev. Neurosci. 39:237–56
     [Google Scholar]
  66. Kohn A, Smith MA 2005. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25:3661–73
     [Google Scholar]
  67. Kording KP, Wolpert DM 2006. Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. 10:319–26
     [Google Scholar]
  68. Kriegeskorte N 2009. Relating population-code representations between man, monkey, and computational models. Front. Neurosci. 3:363–73
     [Google Scholar]
  69. Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C 2008. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320:110–13
     [Google Scholar]
  70. 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. 112:1528–48
     [Google Scholar]
  71. Law C-T, Gold JI 2008. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11:505–13
     [Google Scholar]
  72. Liu LD, Pack CC 2017. The contribution of area MT to visual motion perception depends on training. Neuron 95:436–46.e3
     [Google Scholar]
  73. Luo TZ, Maunsell JHR 2015. Neuronal modulations in visual cortex are associated with only one of multiple components of attention. Neuron 86:1182–88
     [Google Scholar]
  74. Maunsell JHR 2015. Neuronal mechanisms of visual attention. Annu. Rev. Vis. Sci. 1:373–91
     [Google Scholar]
  75. Maunsell JHR, Cook EP 2002. The role of attention in visual processing. Philos. Trans. R. Soc. B 357:1063–72
     [Google Scholar]
  76. Maunsell JHR, Treue S 2006. Feature-based attention in visual cortex. Trends Neurosci 29:317–22
     [Google Scholar]
  77. Mayo JP, Maunsell JHR 2016. Graded neuronal modulations related to visual spatial attention. J. Neurosci. 36:5353–61
     [Google Scholar]
  78. McAdams CJ, Maunsell JHR 1999. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19:431–41
     [Google Scholar]
  79. Miller EK, Buschman TJ 2013. Cortical circuits for the control of attention. Curr. Opin. Neurobiol. 23:216–22
     [Google Scholar]
  80. Miri A, Warriner CL, Seely JS, Elsayed GF, Cunningham JP et al. 2017. Behaviorally selective engagement of short-latency effector pathways by motor cortex. Neuron 95:683–96
     [Google Scholar]
  81. Mitchell JF, Sundberg KA, Reynolds JH 2007. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55:131–41
     [Google Scholar]
  82. Mitchell JF, Sundberg KA, Reynolds JH 2009. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63:879–88
     [Google Scholar]
  83. Moore T, Armstrong KM 2003. Selective gating of visual signals by microstimulation of frontal cortex. Nature 421:370–73
     [Google Scholar]
  84. Moreno-Bote R, Beck J, Kanitscheider I, Pitkow X, Latham P, Pouget A 2014. Information-limiting correlations. Nat. Neurosci. 17:1410–17
     [Google Scholar]
  85. Nandy AS, Nassi JJ, Reynolds JH 2016. Laminar organization of attentional modulation in macaque visual area V4. Neuron 93:235–46
     [Google Scholar]
  86. Ni AM, Ruff DA, Alberts JJ, Symmonds J, Cohen MR 2018. Learning and attention reveal a general relationship between population activity and behavior. Science 359463–65
     [Google Scholar]
  87. Nienborg H, Cohen MR, Cumming BG 2012. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu. Rev. Neurosci. 35:463–83
     [Google Scholar]
  88. Nienborg H, Cumming BG 2009. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 459:89–92
     [Google Scholar]
  89. Oemisch M, Westendorff S, Everling S, Womelsdorf T 2015. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J. Neurosci. 35:13076–89
     [Google Scholar]
  90. Pagan M, Urban LS, Wohl MP, Rust NC 2013. Signals in inferotemporal and perirhinal cortex suggest an untangling of visual target information. Nat. Neurosci. 16:1132–39
     [Google Scholar]
  91. Parker AJ, Newsome WT 1998. Sense and the single neuron: probing the physiology of perception. Annu. Rev. Neurosci. 21:227–77
     [Google Scholar]
  92. Pitkow X, Angelaki DE 2017. Inference in the brain: statistics flowing in redundant population codes. Neuron 94:943–53
     [Google Scholar]
  93. Pitkow X, Liu S, Angelaki DE, DeAngelis GC, Pouget A 2015. How can single sensory neurons predict behavior. Neuron 87:411–23
     [Google Scholar]
  94. Pooresmaeili A, Poort J, Roelfsema PR 2014. Simultaneous selection by object-based attention in visual and frontal cortex. PNAS 111:6467–72
     [Google Scholar]
  95. Poort J, Self MW, van Vugt B, Malkki H, Roelfsema PR 2016. Texture segregation causes early figure enhancement and later ground suppression in areas V1 and V4 of visual cortex. Cereb. Cortex 26:3964–76
     [Google Scholar]
  96. Quian Quiroga R, Panzeri S 2009. Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10:173–85
     [Google Scholar]
  97. Rabinowitz NC, Goris RL, Cohen MR, Simoncelli EP 2015. Attention stabilizes the shared gain of V4 populations. eLife 4:e08998
     [Google Scholar]
  98. Raiguel S, Vogels R, Mysore SG, Orban GA 2006. Learning to see the difference specifically alters the most informative V4 neurons. J. Neurosci. 26:6589–602
     [Google Scholar]
  99. Reynolds JH, Chelazzi L 2004. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27:611–47
     [Google Scholar]
  100. Ruff DA, Alberts JJ, Cohen MR 2016. Relating normalization to neuronal populations across cortical areas. J. Neurophysiol. 116:1375–86
     [Google Scholar]
  101. Ruff DA, Cohen MR 2014a. Attention can either increase or decrease spike count correlations in visual cortex. Nat. Neurosci. 17:1591–97
     [Google Scholar]
  102. Ruff DA, Cohen MR 2014b. Global cognitive factors modulate correlated response variability between V4 neurons. J. Neurosci. 34:16408–16
     [Google Scholar]
  103. Ruff DA, Cohen MR 2016a. Attention increases spike count correlations between visual cortical areas. J. Neurosci. 36:7523–34
     [Google Scholar]
  104. Ruff DA, Cohen MR 2016b. Stimulus dependence of correlated variability across cortical areas. J. Neurosci. 36:7546–56
     [Google Scholar]
  105. Ruff DA, Cohen MR 2017. A normalization model suggests that attention changes the weighting of inputs between visual areas. PNAS 114:E4085–94
     [Google Scholar]
  106. Saalmann Y, Pigarev I, Vidyasagar T 2007. Neural mechanisms of visual attention: how top-down feedback highlights relevant locations. Science 316:1612–15
     [Google Scholar]
  107. Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI et al. 2014. Neural constraints on learning. Nature 512:423–26
     [Google Scholar]
  108. Saproo S, Serences JT 2014. Attention improves transfer of motion information between V1 and MT. J. Neurosci. 34:3586–96
     [Google Scholar]
  109. Semedo J, Zandvakili A, Machens C, Yu BM, Kohn A 2016. Predicting V2 activity from V1 population activity. Cosyne Abstracts 2016 Salt Lake City: UT
     [Google Scholar]
  110. Shadlen MN, Britten KH, Newsome WT, Movshon JA 1996. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16:1486–510
     [Google Scholar]
  111. Smith MA, Kohn A 2008. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28:12591–603
     [Google Scholar]
  112. Smith MA, Sommer MA 2013. Spatial and temporal scales of neuronal correlation in visual area V4. J. Neurosci. 33:5422–32
     [Google Scholar]
  113. Smolyanskaya A, Ruff DA, Born R 2013. Joint tuning for direction of motion and binocular disparity in macaque MT is largely separable. J. Neurophysiol. 110:2806–16
     [Google Scholar]
  114. Solomon SS, Chen SC, Morley JW, Solomon SG 2015. Local and global correlations between neurons in the middle temporal area of primate visual cortex. Cereb. Cortex 25:3182–96
     [Google Scholar]
  115. Sprague TC, Saproo S, Serences JT 2015. Visual attention mitigates information loss in small- and large-scale neural codes. Trends Cogn. Sci. 19:215–26
     [Google Scholar]
  116. Verhoef BE, Maunsell JHR 2017. Attention-related changes in correlated neuronal activity arise from normalization mechanisms. Nat. Neurosci. 20:969–77
     [Google Scholar]
  117. Wimmer K, Compte A, Roxin A, Peixoto D, Renart A, de la Rocha J 2015. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Nat. Commun. 6:6177
     [Google Scholar]
  118. Womelsdorf T, Fries P 2007. The role of neuronal synchronization in selective attention. Curr. Opin. Neurobiol. 17:154–60
     [Google Scholar]
  119. Womelsdorf T, Fries P, Mitra PP, Desimone R 2006. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature 439:733–36
     [Google Scholar]
  120. Yamins DL, DiCarlo JJ 2016. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19:356–65
     [Google Scholar]
  121. Yamins DL, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ 2014. Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111:8619–24
     [Google Scholar]
  122. Yan Y, Rasch MJ, Chen M, Xiang X, Huang M et al. 2014. Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat. Neurosci. 17:1380–87
     [Google Scholar]
  123. Yantis S, Serences JT 2003. Cortical mechanisms of space-based and object-based attentional control. Curr. Opin. Neurobiol. 13:187–93
     [Google Scholar]
  124. Yuste R 2015. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16:487–97
     [Google Scholar]
  125. Zénon A, Krauzlis R 2012. Attention deficits without cortical neuronal deficits. Nature 489:434–37
     [Google Scholar]
  126. Zohary E, Shadlen M, Newsome W 1994. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370:140–43
     [Google Scholar]
/content/journals/10.1146/annurev-neuro-080317-061936
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Cognition as a Window into Neuronal Population Space
Annual Review of Neuroscience 41, 77 (2018); https://doi.org/10.1146/annurev-neuro-080317-061936
/content/journals/10.1146/annurev-neuro-080317-061936
/content/journals/10.1146/annurev-neuro-080317-061936
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