1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Neuroscience
  4. Volume 44, 2021
  5. Article

Annual Review of Neuroscience

Volume 44, 2021

The Geometry of Information Coding in Correlated Neural Populations

Abstract

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.

Keyword(s): correlations, neural coding, neural computation
Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-120320-082744
2021-07-08
2024-04-24
Loading full text...

Full text loading...

/deliver/fulltext/neuro/44/1/annurev-neuro-120320-082744.html?itemId=/content/journals/10.1146/annurev-neuro-120320-082744&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. Averbeck BB, Latham PE, Pouget A. 2006. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7:358–66
     [Google Scholar]
  3. Averbeck BB, Lee D. 2003. Neural noise and movement-related codes in the macaque supplementary motor area. J. Neurosci. 23:7630–41
     [Google Scholar]
  4. Averbeck BB, Lee D. 2004. Coding and transmission of information by neural ensembles. Trends Neurosci 27:225–30
     [Google Scholar]
  5. Averbeck BB, Lee D. 2006. Effects of noise correlations on information encoding and decoding. J. Neurophysiol. 95:3633–44
     [Google Scholar]
  6. Azeredo da Silveira R, Berry MJ II 2014. High-fidelity coding with correlated neurons. PLOS Comput. Biol. 10:e1003970
     [Google Scholar]
  7. 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]
  8. Bartolo R, Saunders RC, Mitz AR, Averbeck BB. 2020. Information-limiting correlations in large neural populations. J. Neurosci. 40:1668–78
     [Google Scholar]
  9. Bethge M, Rotermund D, Pawelzik K. 2002. Optimal short-term population coding: when fisher information fails. Neural Comput 14:2317–51
     [Google Scholar]
  10. Brunel N, Nadal JP. 1998. Mutual information, Fisher information, and population coding. Neural Comput 10:1731–57
     [Google Scholar]
  11. Cayco-Gajic NA, Zylberberg J, Shea-Brown E. 2015. Triplet correlations among similarly tuned cells impact population coding. Front. Comput. Neurosci. 9:57
     [Google Scholar]
  12. Cover TM, Thomas JA. 1999. Elements of Information Theory Hoboken, NJ: John Wiley & Sons
  13. de la Rocha J, Doiron B, Shea-Brown E, Josic K, Reyes A 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802–806
     [Google Scholar]
  14. Doiron B, Litwin-Kumar A, Rosenbaum R, Ocker GK, Josić K. 2016. The mechanics of state-dependent neural correlations. Nat. Neurosci. 19:383–93
     [Google Scholar]
  15. Ecker AS, Berens P, Keliris GA, Bethge M, Logothetis NK, Tolias A. 2010. Decorrelated neuronal firing in cortical microcircuits. Science 327:584–87
     [Google Scholar]
  16. Ecker AS, Berens P, Tolias AS, Bethge M. 2011. The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci. 31:14272–83
     [Google Scholar]
  17. Fiser J, Chiu C, Weliky M. 2004. Small modulation of ongoing cortical dynamics by sensory input during natural vision. Nature 431:573–78
     [Google Scholar]
  18. Franke F, Fiscella M, Sevelev M, Roska B, Hierlemann A, da Silveira RA. 2016. Structures of neural correlation and how they favor coding. Neuron 89:409–22
     [Google Scholar]
  19. Golledge HD, Panzeri S, Zheng F, Pola G, Scannell JW et al. 2003. Correlations, feature-binding and population coding in primary visual cortex. Neuroreport 14:1045–50
     [Google Scholar]
  20. Goris RL, Movshon JA, Simoncelli EP. 2014. Partitioning neuronal variability. Nat. Neurosci. 17:858–65
     [Google Scholar]
  21. Graf AB, Kohn A, Jazayeri M, Movshon JA. 2011. Decoding the activity of neuronal populations in macaque primary visual cortex. Nat. Neurosci. 14:239–45
     [Google Scholar]
  22. Hatsopoulos NG, Ojakangas CL, Paninski L, Donoghue JP 1998. Information about movement direction obtained from synchronous activity of motor cortical neurons. PNAS 95:15706–11
     [Google Scholar]
  23. Hu Y, Zylberberg J, Shea-Brown E. 2014. The sign rule and beyond: Boundary effects, flexibility, and noise correlations in neural population codes. PLOS Comput. Biol. 10:e1003469
     [Google Scholar]
  24. 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
     [Google Scholar]
  25. Hubel DH. 1995. Eye, Brain, and Vision. New York: Scientific American
  26. Johnson KO. 1980. Sensory discrimination: decision process. J. Neurophysiol. 43:1771–92
     [Google Scholar]
  27. Josic K, Shea-Brown E, Doiron B, de la Rocha J. 2009. Stimulus-dependent correlations and population codes. Neural Comput 21:2774–804
     [Google Scholar]
  28. Kang K, Sompolinsky H. 2001. Mutual information of population codes and distance measures in probability space. Phys. Rev. Lett. 86:4958
     [Google Scholar]
  29. Kohn A, Coen-Cagli R, Kanitscheider I, Pouget A. 2016. Correlations and neuronal population information. Annu. Rev. Neurosci. 39:237–56
     [Google Scholar]
  30. Kohn A, Smith MA. 2005. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25:3661–73
     [Google Scholar]
  31. Lee D, Port NL, Kruse W, Georgopoulos AP. 1998. Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex. J. Neurosci. 18:1161–70
     [Google Scholar]
  32. Lin IC, Okun M, Carandini M, Harris KD. 2015. The nature of shared cortical variability. Neuron 87:644–56
     [Google Scholar]
  33. Mastrogiuseppe F, Ostojic S. 2018. Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron 99:609–23
     [Google Scholar]
  34. Mastronarde DN. 1989. Correlated firing of retinal ganglion cells. Trends Neurosci 12:75–80
     [Google Scholar]
  35. Montani F, Kohn A, Smith MA, Schultz SR. 2007. The role of correlations in direction and contrast coding in the primary visual cortex. J. Neurosci. 27:2338–48
     [Google Scholar]
  36. Montijn JS, Meijer GT, Lansink CS, Pennartz CM. 2016. Population-level neural codes are robust to single-neuron variability from a multidimensional coding perspective. Cell Rep 16:2486–98
     [Google Scholar]
  37. Moreno-Bote R, Beck J, Kanitscheider I, Pitkow X, Latham P, Pouget A. 2014. Information-limiting correlations. Nat. Neurosci. 17:1410–17
     [Google Scholar]
  38. Nigam S, Pojoga S, Dragoi V. 2019. Synergistic coding of visual information in columnar networks. Neuron 104:402–11
     [Google Scholar]
  39. Ocker GK, Josić K, Shea-Brown E, Buice MA. 2017. Linking structure and activity in nonlinear spiking networks. PLOS Comput. Biol. 13:e1005583
     [Google Scholar]
  40. Oram MW, Foldiak P, Perrett DI, Sengpiel F. 1998. The ‘ideal homunculus’: decoding neural population signals. Trends Neurosci 21:259–65
     [Google Scholar]
  41. Ostojic S, Brunel N, Hakim V. 2009. How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. J. Neurosci. 29:10234–53
     [Google Scholar]
  42. Ozden I, Lee HM, Sullivan MR, Wang SS. 2008. Identification and clustering of event patterns from in vivo multiphoton optical recordings of neuronal ensembles. J. Neurophysiol. 100:495–503
     [Google Scholar]
  43. Panzeri S, Schultz SR, Treves A, Rolls ET. 1999. Correlations and the encoding of information in the nervous system. Proc. R. Soc. B 266:1001–12
     [Google Scholar]
  44. Perkel DH, Gerstein GL, Moore GP. 1967. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys. J. 7:419–40
     [Google Scholar]
  45. Pernice V, da Silveira RA. 2018. Interpretation of correlated neural variability from models of feed-forward and recurrent circuits. PLOS Comput. Biol. 14:e1005979
     [Google Scholar]
  46. Pernice V, Staude B, Cardanobile S, Rotter S. 2011. How structure determines correlations in neuronal networks. PLOS Comput. Biol. 7:e1002059
     [Google Scholar]
  47. Pola G, Thiele A, Hoffmann KP, Panzeri S. 2003. An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network 14:35–60
     [Google Scholar]
  48. 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]
  49. 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]
  50. Rosenbaum R, Smith MA, Kohn A, Rubin JE, Doiron B. 2017. The spatial structure of correlated neuronal variability. Nat. Neurosci. 20:107–14
     [Google Scholar]
  51. Rumyantsev OI, Lecoq JA, Hernandez O, Zhang Y, Savall J et al. 2020. Fundamental bounds on the fidelity of sensory cortical coding. Nature 580:100–5
     [Google Scholar]
  52. Sasaki K, Bower JM, Llinas R. 1989. Multiple Purkinje cell recording in rodent cerebellar cortex. Eur. J. Neurosci. 1:572–86
     [Google Scholar]
  53. Schuessler F, Dubreuil A, Mastrogiuseppe F, Ostojic S, Barak O. 2020. Dynamics of random recurrent networks with correlated low-rank structure. Phys. Rev. Res. 2:013111
     [Google Scholar]
  54. Shamir M, Sompolinsky H. 2004. Nonlinear population codes. Neural Comput 16:1105–36
     [Google Scholar]
  55. Shamir M, Sompolinsky H. 2006. Implications of neuronal diversity on population coding. Neural Comput 18:1951–86
     [Google Scholar]
  56. Shlens J, Rieke F, Chichilnisky E. 2008. Synchronized firing in the retina. Curr. Opin. Neurobiol. 18:396–402
     [Google Scholar]
  57. Smith MA, Kohn A. 2008. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28:12591–603
     [Google Scholar]
  58. Sompolinsky H, Yoon H, Kang K, Shamir M. 2001. Population coding in neuronal systems with correlated noise. Phys. Rev. E 64:051904
     [Google Scholar]
  59. Sreenivasan S, Fiete I. 2011. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat. Neurosci. 14:1330
     [Google Scholar]
  60. Stringer C, Michaelos M, Pachitariu M. 2019. High precision coding in visual cortex. bioRxiv 679324. https://doi.org/10.1101/679324
    [Crossref]
  61. Tannenbaum NR, Burak Y. 2017. Theory of nonstationary Hawkes processes. Phys. Rev. E 96:062314
     [Google Scholar]
  62. Trousdale J, Hu Y, Shea-Brown E, Josić K. 2012. Impact of network structure and cellular response on spike time correlations. PLOS Comput. Biol. 8:e1002408
     [Google Scholar]
  63. Usrey WM, Reid RC. 1999. Synchronous activity in the visual system. Annu. Rev. Physiol. 61:435–56
     [Google Scholar]
  64. Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y et al. 1995. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373:515–18
     [Google Scholar]
  65. Vidne M, Ahmadian Y, Shlens J, Pillow J, Kulkarni J et al. 2012. Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. 33:97–121
     [Google Scholar]
  66. Vogels R. 1990. Population coding of stimulus orientation by striate cortical cells. Biol. Cybern. 64:25–31
     [Google Scholar]
  67. Wei X-X, Stocker AA. 2016. Mutual information, Fisher information, and efficient coding. Neural Comput 28:305–26
     [Google Scholar]
  68. Wilke SD, Eurich CW. 2002. Representational accuracy of stochastic neural populations. Neural Comput 14:155–89
     [Google Scholar]
  69. Zohary E, Shadlen MN, Newsome WT. 1994. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370:140–43
     [Google Scholar]
  70. Zylberberg J. 2018. The role of untuned neurons in sensory information coding. bioRxiv 134379. https://doi.org/10.1101/134379
    [Crossref]
  71. Zylberberg J, Cafaro J, Turner MH, Shea-Brown E, Rieke F. 2016. Direction-selective circuits shape noise to ensure a precise population code. Neuron 89:369–83
     [Google Scholar]
  72. Zylberberg J, Shea-Brown E. 2015. Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations. Phys. Rev. E 92:062707
     [Google Scholar]
/content/journals/10.1146/annurev-neuro-120320-082744
Loading
The Geometry of Information Coding in Correlated Neural Populations
Annual Review of Neuroscience 44, 403 (2021); https://doi.org/10.1146/annurev-neuro-120320-082744
/content/journals/10.1146/annurev-neuro-120320-082744
/content/journals/10.1146/annurev-neuro-120320-082744
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Article Type: Review Article

Most Cited Most Cited RSS feed

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