1932

Abstract

Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-vision-030320-041223
2020-09-15
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/vision/6/1/annurev-vision-030320-041223.html?itemId=/content/journals/10.1146/annurev-vision-030320-041223&mimeType=html&fmt=ahah

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. Afraz SR, Kiani R, Esteky H 2006. Microstimulation of inferotemporal cortex influences face categorization. Nature 442:692–95
    [Google Scholar]
  3. Ahmed B, Cordery PM, McLelland D, Bair W, Krug K 2012. Long-range clustered connections within extrastriate visual area V5/MT of the rhesus macaque. Cereb. Cortex 22:60–73
    [Google Scholar]
  4. Amir Y, Harel M, Malach R 1993. Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual-cortex. J. Comp. Neurol. 334:19–46
    [Google Scholar]
  5. Andersen RA, Cui H. 2009. Intention, action planning, and decision making in parietal-frontal circuits. Neuron 63:568–83
    [Google Scholar]
  6. Andrei AR, Pojoga S, Janz R, Dragoi V 2019. Integration of cortical population signals for visual perception. Nat. Commun. 10:3832
    [Google Scholar]
  7. Angelucci A, Levitt JB, Lund JS 2002a. Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1. Prog. Brain Res. 136:373–88
    [Google Scholar]
  8. Angelucci A, Levitt JB, Walton EJ, Hupe JM, Bullier J, Lund JS 2002b. Circuits for local and global signal integration in primary visual cortex. J. Neurosci. 22:8633–46
    [Google Scholar]
  9. Arcaro MJ, Honey CJ, Mruczek RE, Kastner S, Hasson U 2015. Widespread correlation patterns of fMRI signal across visual cortex reflect eccentricity organization. eLife 4:e03952
    [Google Scholar]
  10. Armstrong KM, Moore T. 2007. Rapid enhancement of visual cortical response discriminability by microstimulation of the frontal eye field. PNAS 104:9499–504
    [Google Scholar]
  11. 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]
  12. Barlow HB. 1953. Summation and inhibition in the frog's retina. J. Physiol. 119:69–88
    [Google Scholar]
  13. Barlow HB. 1972. Single units and sensation: a neuron doctrine for perceptual psychology. Perception 1:371–94
    [Google Scholar]
  14. Barlow HB, Blakemore C, Pettigrew JD 1967. The neural mechanism of binocular depth discrimination. J. Physiol. 193:327–42
    [Google Scholar]
  15. Baruni JK, Lau B, Salzman CD 2015. Reward expectation differentially modulates attentional behavior and activity in visual area V4. Nat. Neurosci. 18:1656–63
    [Google Scholar]
  16. Bettle R 2014. Organisation and evolution of the input into the middle temporal area of the macaque. B.A. Diss., Final Honours School Biol. Sci., Oxford Univ., UK
    [Google Scholar]
  17. Bondy AG, Haefner RM, Cumming BG 2018. Feedback determines the structure of correlated variability in primary visual cortex. Nat. Neurosci. 21:598–606
    [Google Scholar]
  18. Bosking WH, Maunsell JHR. 2011. Effects of stimulus direction on the correlation between behavior and single units in area MT during a motion detection task. J. Neurosci. 31:8230–38
    [Google Scholar]
  19. Braun J, Mattia M. 2010. Attractors and noise: twin drivers of decisions and multistability. NeuroImage 52:740–51
    [Google Scholar]
  20. 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]
  21. Britten KH, van Wezel RJA 2002. Area MST and heading perception in macaque monkeys. Cereb. Cortex 12:692–701
    [Google Scholar]
  22. Brosch M, Selezneva E, Scheich H 2011. Representation of reward feedback in primate auditory cortex. Front. Syst. Neurosci. 5:5
    [Google Scholar]
  23. Cao R, Pastukhov A, Mattia M, Braun J 2016. Collective activity of many bistable assemblies reproduces characteristic dynamics of multistable perception. J. Neurosci. 36:6957–72
    [Google Scholar]
  24. Celebrini S, Newsome WT. 1994. Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J. Neurosci. 14:4109–24
    [Google Scholar]
  25. Cicmil N, Cumming BG, Parker AJ, Krug K 2015. Reward modulates the effect of visual cortical microstimulation on perceptual decisions. eLife 4:e07832
    [Google Scholar]
  26. Cicmil N, Krug K. 2015. Playing the electric light orchestra: how electrical stimulation of visual cortex elucidates the neural basis of perception. Phil. Trans. R. Soc. Lond. B 370:20140206
    [Google Scholar]
  27. Cohen MR, Maunsell JH. 2009. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12:1594–600
    [Google Scholar]
  28. Cohen MR, Newsome WT. 2008. Context-dependent changes in functional circuitry in visual area MT. Neuron 60:162–73
    [Google Scholar]
  29. 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]
  30. Colby CL, Goldberg ME. 1999. Space and attention in parietal cortex. Annu. Rev. Neurosci. 22:319–49
    [Google Scholar]
  31. de Lafuente V, Jazayeri M, Shadlen MN 2015. Representation of accumulating evidence for a decision in two parietal areas. J. Neurosci. 35:4306–18
    [Google Scholar]
  32. DeAngelis GC, Cumming BG, Newsome WT 1998. Cortical area MT and the perception of stereoscopic depth. Nature 394:677–80
    [Google Scholar]
  33. DeAngelis GC, Newsome WT. 1999. Organization of disparity-selective neurons in macaque area MT. J. Neurosci. 19:1398–415
    [Google Scholar]
  34. DeAngelis GC, Newsome WT. 2004. Perceptual “read-out” of conjoined direction and disparity maps in extrastriate area MT. PLOS Biol 2:394–404
    [Google Scholar]
  35. Ding L, Gold JI. 2012. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22:1052–67
    [Google Scholar]
  36. Dodd JV, Krug K, Cumming BG, Parker AJ 2001. Perceptually bistable three-dimensional figures evoke high choice probabilities in cortical area. J. Neurosci. 21:4809–21
    [Google Scholar]
  37. Doiron B, Litwin-Kumar A, Rosenbaum R, Ocker GK, Josic K 2016. The mechanics of state-dependent neural correlations. Nat. Neurosci. 19:383–93
    [Google Scholar]
  38. Douglas RJ, Martin KA. 2004. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27:419–51
    [Google Scholar]
  39. Drebitz E, Haag M, Grothe I, Mandon S, Kreiter AK 2018. Attention configures synchronization within local neuronal networks for processing of the behaviorally relevant stimulus. Front. Neural Circuits 12:71
    [Google Scholar]
  40. Dubner R, Zeki SM. 1971. Response properties and receptive fields of cells in an anatomically-defined region of the superior temporal sulcus in the monkey. Brain Res 35:528–32
    [Google Scholar]
  41. 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]
  42. Engel TA, Steinmetz NA, Gieselmann MA, Thiele A, Moore T, Boahen K 2016. Selective modulation of cortical state during spatial attention. Science 354:1140–44
    [Google Scholar]
  43. Esghaei M, Daliri MR, Treue S 2017. Local field potentials are induced by visually evoked spiking activity in macaque cortical area MT. Sci. Rep. 7:17110
    [Google Scholar]
  44. Fan Y, Gold JI, Ding L 2018. Ongoing, rational calibration of reward-driven perceptual biases. eLife 7:e36018
    [Google Scholar]
  45. Felleman DJ, Van Essen DC 1991. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1:1–47
    [Google Scholar]
  46. Fetz EE, Cheney PD, German DC 1976. Corticomotoneuronal connections of precentral cells detected by postspike averages of EMG activity in behaving monkeys. Brain Res 114:505–10
    [Google Scholar]
  47. Fitzgerald JK, Freedman DJ, Fanini A, Bennur S, Gold JI, Assad JA 2013. Biased associative representations in parietal cortex. Neuron 77:180–91
    [Google Scholar]
  48. Freedman DJ, Assad JA. 2006. Experience-dependent representation of visual categories in parietal cortex. Nature 443:85–88
    [Google Scholar]
  49. Freedman DJ, Ibos G. 2018. An integrative framework for sensory, motor, and cognitive functions of the posterior parietal cortex. Neuron 97:1219–34
    [Google Scholar]
  50. Giorgio J, Karlaftis VM, Wang R, Shen Y, Tino P et al. 2018. Functional brain networks for learning predictive statistics. Cortex 107:204–19
    [Google Scholar]
  51. Gold JI, Shadlen MN. 2007. The neural basis of decision making. Annu. Rev. Neurosci. 30:535–74
    [Google Scholar]
  52. Gold JI, Stocker AA. 2017. Visual decision-making in an uncertain and dynamic world. Annu. Rev. Vis. Sci. 3:227–50
    [Google Scholar]
  53. Gore F, Schwartz EC, Salzman CD 2015. Manipulating neural activity in physiologically classified neurons: triumphs and challenges. Philos. Trans. R. Soc. Lond. B 370:20140216
    [Google Scholar]
  54. Grothe I, Neitzel SD, Mandon S, Kreiter AK 2012. Switching neuronal inputs by differential modulations of gamma-band phase-coherence. J. Neurosci. 32:16172–80
    [Google Scholar]
  55. Grothe I, Rotermund D, Neitzel SD, Mandon S, Ernst UA et al. 2018. Attention selectively gates afferent signal transmission to area V4. J. Neurosci. 38:3441–52
    [Google Scholar]
  56. Haefner R, Gerwinn S, Macke J, Bethge M 2013. Inferring decoding strategy from choice probabilities in the presence of noise correlations. Nat. Neurosci. 16:235–42
    [Google Scholar]
  57. Hanks TD, Ditterich J, Shadlen MN 2006. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat. Neurosci. 9:682–89
    [Google Scholar]
  58. Hecht S, Shlaer S, Pirenne MH 1942. Energy, quanta, and vision. J. Gen. Physiol. 25:819–40
    [Google Scholar]
  59. Histed MH, Bonin V, Reid RC 2009. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63:508–22
    [Google Scholar]
  60. Huang C, Ruff DA, Pyle R, Rosenbaum R, Cohen MR, Doiron B 2019. Circuit models of low-dimensional shared variability in cortical networks. Neuron 101:337–48.e4
    [Google Scholar]
  61. Huang Y, Mylius J, Scheich H, Brosch M 2016. Tonic effects of the dopaminergic ventral midbrain on the auditory cortex of awake macaque monkeys. Brain Struct. Funct. 221:969–77
    [Google Scholar]
  62. Hubel DH, Wiesel TN. 1968. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195:215–43
    [Google Scholar]
  63. Ibos G, Freedman DJ. 2014. Dynamic integration of task-relevant visual features in posterior parietal cortex. Neuron 83:1468–80
    [Google Scholar]
  64. Jazayeri M, Lindbloom-Brown Z, Horwitz GD 2012. Saccadic eye movements evoked by optogenetic activation of primate V1. Nat. Neurosci. 15:1368–70
    [Google Scholar]
  65. Johansson RS, Vallbo AB. 1979. Detection of tactile stimuli: thresholds of afferent units related to psychophysical thresholds in the human hand. J. Physiol. 297:405–22
    [Google Scholar]
  66. Katz LN, Yates JL, Pillow JW, Huk AC 2016. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535:285–88
    [Google Scholar]
  67. 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]
  68. Khamechian MB, Kozyrev V, Treue S, Esghaei M, Daliri MR 2019. Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex. PNAS 116:12506–15
    [Google Scholar]
  69. Kiani R, Cueva CJ, Reppas JB, Newsome WT 2014. Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials. Curr. Biol. 24:1542–47
    [Google Scholar]
  70. Krause BM, Ghose GM. 2018. Micropools of reliable area MT neurons explain rapid motion detection. J. Neurophysiol. 120:2396–409
    [Google Scholar]
  71. Kravitz DJ, Saleem KS, Baker CI, Ungerleider LG, Mishkin M 2013. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends Cogn. Sci. 17:26–49
    [Google Scholar]
  72. Krug K. 2004. A common neuronal code for perceptual visual cortex? Comparing choice and processes in attentional correlates in V5/MT. Philos. Trans. R. Soc. Lond. B 359:929–41
    [Google Scholar]
  73. Krug K, Cicmil N, Parker AJ, Cumming BG 2013. A causal role for V5/MT neurons coding motion-disparity conjunctions in resolving perceptual ambiguity. Curr. Biol. 23:1454–59
    [Google Scholar]
  74. Krug K, Cumming BG, Parker AJ 2004. Comparing perceptual signals of single V5/MT neurons in two binocular depth tasks. J. Neurophysiol. 92:1586–96
    [Google Scholar]
  75. Krug K, Curnow TL, Parker AJ 2016. Defining the V5/MT neuronal pool for perceptual decisions in a visual stereo-motion task. Philos. Trans. R. Soc. Lond. B 371:20150260
    [Google Scholar]
  76. Latimer KW, Yates JL, Meister ML, Huk AC, Pillow JW 2015. Neuronal modeling: Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349:184–87
    [Google Scholar]
  77. Lewis CM, Bosman CA, Womelsdorf T, Fries P 2016. Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization. PNAS 113:E606–15
    [Google Scholar]
  78. 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]
  79. Lund JS, Angelucci A, Bressloff PC 2003. Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb. Cortex 13:15–24
    [Google Scholar]
  80. Mante V, Sussillo D, Shenoy KV, Newsome WT 2013. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503:78–84
    [Google Scholar]
  81. Mattia M, Pani P, Mirabella G, Costa S, Del Giudice P, Ferraina S 2013. Heterogeneous attractor cell assemblies for motor planning in premotor cortex. J. Neurosci. 33:11155–68
    [Google Scholar]
  82. Maunsell JHR. 2015. Neuronal mechanisms of visual attention. Annu. Rev. Vis. Sci. 1:373–91
    [Google Scholar]
  83. Merrikhi Y, Clark K, Albarran E, Parsa M, Zirnsak M et al. 2017. Spatial working memory alters the efficacy of input to visual cortex. Nat. Commun. 8:15041
    [Google Scholar]
  84. Mountcastle VB, Talbot WH, Darian-Smith I, Kornhuber HH 1967. Neural basis of the sense of flutter-vibration. Science 155:597–600
    [Google Scholar]
  85. Movshon JA, Thompson ID, Tolhurst DJ 1978. Spatial summation in the receptive fields of simple cells in the cat's striate cortex. J. Physiol. 283:53–77
    [Google Scholar]
  86. Murasugi CM, Salzman CD, Newsome WT 1993. Microstimulation in visual area MT: effects of varying pulse amplitude and frequency. J. Neurosci. 13:1719–29
    [Google Scholar]
  87. Newsome WT, Britten KH, Movshon JA 1989. Neuronal correlates of a perceptual decision. Nature 341:52–54
    [Google Scholar]
  88. Nichols MJ, Newsome WT. 2002. Middle temporal visual area microstimulation influences veridical judgments of motion direction. J. Neurosci. 22:9530–40
    [Google Scholar]
  89. 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]
  90. 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]
  91. Ostojic S. 2014. Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nat. Neurosci. 17:594–600
    [Google Scholar]
  92. Parker AJ. 2013. A micro-pool model for decision-related signals in visual cortical areas. Front. Comput. Neurosci. 7:115
    [Google Scholar]
  93. Parker AJ, Krug K, Cumming BG 2002. Neuronal activity and its links with the perception of multi-stable figures. Philos. Trans. R. Soc. Lond. B 357:1053–62
    [Google Scholar]
  94. Parker AJ, Newsome WT. 1998. Sense and the single neuron: probing the physiology of perception. Annu. Rev. Neurosci. 21:227–77
    [Google Scholar]
  95. Peck CJ, Salzman CD. 2014. The amygdala and basal forebrain as a pathway for motivationally guided attention. J. Neurosci. 34:13757–67
    [Google Scholar]
  96. Pitkow X, Liu S, Angelaki DE, DeAngelis GC, Pouget A 2015. How can single sensory neurons predict behavior. Neuron 87:411–23
    [Google Scholar]
  97. Ponce-Alvarez A, Thiele A, Albright TD, Stoner GR, Deco G 2013. Stimulus-dependent variability and noise correlations in cortical MT neurons. PNAS 110:13162–67
    [Google Scholar]
  98. Purushothaman G, Bradley DC. 2005. Neural population code for fine perceptual decisions in area MT. Nat. Neurosci. 8:99–106
    [Google Scholar]
  99. Rockland KS. 1995. Morphology of individual axons projecting from area V2 to MT in the macaque. J. Comp. Neurol. 355:15–26
    [Google Scholar]
  100. Rohenkohl G, Bosman CA, Fries P 2018. Gamma synchronization between V1 and V4 improves behavioral performance. Neuron 100:953–63.e3
    [Google Scholar]
  101. Roitman JD, Shadlen MN. 2002. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22:9475–89
    [Google Scholar]
  102. Romo R, Hernandez A, Zainos A, Salinas E 2003. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38:649–57
    [Google Scholar]
  103. Ronzitti E, Conti R, Zampini V, Tanese D, Foust AJ et al. 2017. Submillisecond optogenetic control of neuronal firing with two-photon holographic photoactivation of Chronos. J. Neurosci. 37:10679–89
    [Google Scholar]
  104. Ruff DA, Cohen MR. 2014. Attention can either increase or decrease spike count correlations in visual cortex. Nat. Neurosci. 17:1591–97
    [Google Scholar]
  105. Ruff DA, Cohen MR. 2016a. Attention increases spike count correlations between visual cortical areas. J. Neurosci. 36:7523–34
    [Google Scholar]
  106. Ruff DA, Cohen MR. 2016b. Stimulus dependence of correlated variability across cortical areas. J. Neurosci. 36:7546–56
    [Google Scholar]
  107. 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]
  108. Rust NC, Mante V, Simoncelli EP, Movshon JA 2006. How MT cells analyze the motion of visual patterns. Nat. Neurosci. 9:1421–31
    [Google Scholar]
  109. Salzman CD, Britten KH, Newsome WT 1990. Cortical microstimulation influences perceptual judgments of motion direction. Nature 346:174–77
    [Google Scholar]
  110. Semedo JD, Zandvakili A, Machens CK, Yu BM, Kohn A 2019. Cortical areas interact through a communication subspace. Neuron 102:249–59.e4
    [Google Scholar]
  111. 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]
  112. Shadlen MN, Newsome WT. 2001. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86:1916–36
    [Google Scholar]
  113. Sherrington CS. 1906. The Integrative Action of the Nervous System London: Constable
  114. Shiozaki HM, Tanabe S, Doi T, Fujita I 2012. Neural activity in cortical area V4 underlies fine disparity discrimination. J. Neurosci. 32:3830–41
    [Google Scholar]
  115. Shushruth S, Mazurek M, Shadlen MN 2018. Comparison of decision-related signals in sensory and motor preparatory responses of neurons in area LIP. J. Neurosci. 38:6350–65
    [Google Scholar]
  116. Smith JET, Parker AJ. 2019. Time-dependent processing of correlated signals between visual cortical areas improves neuronal population encoding in a sensory discrimination task. bioRxiv 844514. https://doi.org/10.1101/844514
    [Crossref]
  117. Stauffer WR, Lak A, Yang A, Borel M, Paulsen O et al. 2016. Dopamine neuron-specific optogenetic stimulation in rhesus macaques. Cell 166:1564–71.e6
    [Google Scholar]
  118. Tsunada J, Cohen Y, Gold JI 2019. Post-decision processing in primate prefrontal cortex influences subsequent choices on an auditory decision-making task. eLife 8:e46770
    [Google Scholar]
  119. Uka T, DeAngelis GC. 2004. Contribution of area MT to stereoscopic depth perception: Choice-related response modulations reflect task strategy. Neuron 42:297–310
    [Google Scholar]
  120. Uka T, DeAngelis GC. 2006. Linking neural representation to function in stereoscopic depth perception: roles of the middle temporal area in coarse versus fine disparity discrimination. J. Neurosci. 26:6791–802
    [Google Scholar]
  121. Verhagen L, Gallea C, Folloni D, Constans C, Jensen DE et al. 2019. Offline impact of transcranial focused ultrasound on cortical activation in primates. eLife 8:e40541
    [Google Scholar]
  122. Verhoef BE, Vogels R, Janssen P 2015. Effects of microstimulation in the anterior intraparietal area during three-dimensional shape categorization. PLOS ONE 10:e0136543
    [Google Scholar]
  123. Wang XJ. 2008. Decision making in recurrent neuronal circuits. Neuron 60:215–34
    [Google Scholar]
  124. Wasmuht DF, Parker AJ, Krug K 2019. Interneuronal correlations at longer time scales predict decision signals for bistable structure-from-motion perception. Sci. Rep. 9:11449
    [Google Scholar]
  125. 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]
  126. Yu X, Gu Y. 2018. Probing sensory readout via combined choice-correlation measures and microstimulation perturbation. Neuron 100:715–27.e5
    [Google Scholar]
  127. Zaidel A, DeAngelis GC, Angelaki DE 2017. Decoupled choice-driven and stimulus-related activity in parietal neurons may be misrepresented by choice probabilities. Nat. Commun. 8:715
    [Google Scholar]
  128. Zhou Y, Freedman DJ. 2019. Posterior parietal cortex plays a causal role in perceptual and categorical decisions. Science 365:180–85
    [Google Scholar]
  129. Zohary E, Shadlen MN, Newsome WT 1994. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370:140–43
    [Google Scholar]
/content/journals/10.1146/annurev-vision-030320-041223
Loading
/content/journals/10.1146/annurev-vision-030320-041223
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