Table of Contents
Advances in Neuroscience
Volume 2014 (2014), Article ID 907851, 14 pages
http://dx.doi.org/10.1155/2014/907851
Review Article

Understanding Neural Population Coding: Information Theoretic Insights from the Auditory System

1Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068 Rovereto, Italy
2Doctoral School in Cognitive and Brain Sciences, University of Trento, Via Bettini 31, 38068 Rovereto, Italy
3Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
4Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany
5Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany

Received 30 April 2014; Accepted 31 July 2014; Published 19 October 2014

Academic Editor: Xiang-Ping Chu

Copyright © 2014 Arno Onken et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Ferster and N. Spruston, “Cracking the neuronal code,” Science, vol. 270, no. 5237, pp. 756–757, 1995. View at Publisher · View at Google Scholar · View at Scopus
  2. C. F. Stevens and A. Zador, “The enigma of the brain,” Current Biology, vol. 5, no. 12, pp. 1370–1371, 1995. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Rieke, Spikes: Exploring the Neural Code, The MIT Press, Cambridge, Mass, USA, 1999.
  4. S. Panzeri, N. Brunel, N. K. Logothetis, and C. Kayser, “Sensory neural codes using multiplexed temporal scales,” Trends in Neurosciences, vol. 33, no. 3, pp. 111–120, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. W. B. Kristan Jr. and B. K. Shaw, “Population coding and behavioral choice,” Current Opinion in Neurobiology, vol. 7, no. 6, pp. 826–831, 1997. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Pouget, P. Dayan, and R. Zemel, “Information processing with population codes,” Nature Reviews Neuroscience, vol. 1, no. 2, pp. 125–132, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. J. D. Victor, “How the brain uses time to represent and process visual information,” Brain Research, vol. 886, no. 1-2, pp. 33–46, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Foffani, J. K. Chapin, and K. A. Moxon, “Computational role of large receptive fields in the primary somatosensory cortex,” Journal of Neurophysiology, vol. 100, no. 1, pp. 268–280, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Gollisch and M. Meister, “Rapid neural coding in the retina with relative spike latencies,” Science, vol. 319, no. 5866, pp. 1108–1111, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Shusterman, M. C. Smear, A. A. Koulakov, and D. Rinberg, “Precise olfactory responses tile the sniff cycle,” Nature Neuroscience, vol. 14, no. 8, pp. 1039–1044, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Panzeri, R. Senatore, M. A. Montemurro, and R. S. Petersen, “Correcting for the sampling bias problem in spike train information measures,” Journal of Neurophysiology, vol. 98, no. 3, pp. 1064–1072, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. R. A. A. Ince, R. Senatore, E. Arabzadeh, F. Montani, M. E. Diamond, and S. Panzeri, “Information-theoretic methods for studying population codes,” Neural Networks, vol. 23, no. 6, pp. 713–727, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Nelken and G. Chechik, “Information theory in auditory research,” Hearing Research, vol. 229, no. 1-2, pp. 94–105, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Q. Quiroga and S. Panzeri, “Extracting information from neuronal populations: information theory and decoding approaches,” Nature Reviews Neuroscience, vol. 10, no. 3, pp. 173–185, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. C. E. Shannon, “The mathematical theory of communication,” The Bell System Technical Journal, vol. 27, pp. 379–423, 623–656, 1948. View at Publisher · View at Google Scholar · View at MathSciNet
  16. J. D. Victor and K. P. Purpura, “Nature and precision of temporal coding in visual cortex: a metric-space analysis,” Journal of Neurophysiology, vol. 76, no. 2, pp. 1310–1326, 1996. View at Google Scholar · View at Scopus
  17. R. R. D. R. van Steveninck, G. D. Lewen, S. P. Strong, R. Koberle, and W. Bialek, “Reproducibility and variability in neural spike trains,” Science, vol. 275, no. 5307, pp. 1805–1808, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Kayser, M. A. Montemurro, N. K. Logothetis, and S. Panzeri, “Spike-phase coding boosts and stabilizes the information carried by spatial and temporal spike patterns,” Neuron, vol. 61, no. 4, pp. 597–608, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Kayser, N. K. Logothetis, and S. Panzeri, “Millisecond encoding precision of auditory cortex neurons,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 39, pp. 16976–16981, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Panzeri, R. S. Petersen, S. R. Schultz, M. Lebedev, and M. E. Diamond, “The role of spike timing in the coding of stimulus location in rat somatosensory cortex,” Neuron, vol. 29, no. 3, pp. 769–777, 2001. View at Publisher · View at Google Scholar · View at Scopus
  21. J. A. Garcia-Lazaro, L. A. Belliveau, and N. A. Lesica, “Independent population coding of speech with sub-millisecond precision,” The Journal of Neuroscience, vol. 33, no. 49, pp. 19362–19372, 2013. View at Publisher · View at Google Scholar
  22. C. A. Perez, C. T. Engineer, V. Jakkamsetti, R. S. Carraway, M. S. Perry, and M. P. Kilgard, “Different timescales for the neural coding of consonant and vowel sounds,” Cerebral Cortex, vol. 23, no. 3, pp. 670–683, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. E. Diamond, M. von Heimendahl, and E. Arabzadeh, “Whisker-mediated texture discrimination,” PLoS Biology, vol. 6, article e220, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. M. E. Diamond and E. Arabzadeh, “Whisker sensory system—from receptor to decision,” Progress in Neurobiology, vol. 103, pp. 28–40, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. K. P. Purpura, S. F. Kalik, and N. D. Schiff, “Analysis of perisaccadic field potentials in the occipitotemporal pathway during active vision,” Journal of Neurophysiology, vol. 90, no. 5, pp. 3455–3478, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. C. E. Schroeder, D. A. Wilson, T. Radman, H. Scharfman, and P. Lakatos, “Dynamics of active sensing and perceptual selection,” Current Opinion in Neurobiology, vol. 20, no. 2, pp. 172–176, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. J. D. Moore, M. Deschênes, T. Furuta et al., “Hierarchy of orofacial rhythms revealed through whisking and breathing,” Nature, vol. 497, no. 7448, pp. 205–210, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Brasselet, S. Panzeri, N. K. Logothetis, and C. Kayser, “Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex,” Journal of Neuroscience, vol. 32, no. 9, pp. 2998–3008, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Panzeri, R. A. Ince, M. E. Diamond, and C. Kayser, “Reading spike timing without a clock: intrinsic decoding of spike trains,” Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, vol. 369, no. 1637, 2014. View at Publisher · View at Google Scholar
  30. C. T. Engineer, C. A. Perez, Y. H. Chen et al., “Cortical activity patterns predict speech discrimination ability,” Nature Neuroscience, vol. 11, no. 5, pp. 603–608, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. S. M. Chase and E. D. Young, “First-spike latency information in single neurons increases when referenced to population onset,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 12, pp. 5175–5180, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. C. Kayser, R. A. A. Ince, and S. Panzeri, “Analysis of slow (theta) oscillations as a potential temporal reference frame for information coding in sensory cortices,” PLoS Computational Biology, vol. 8, no. 10, Article ID e1002717, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. S. A. Shamma, M. Elhilali, and C. Micheyl, “Temporal coherence and attention in auditory scene analysis,” Trends in Neurosciences, vol. 34, no. 3, pp. 114–123, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Giraud and D. Poeppel, “Cortical oscillations and speech processing: emerging computational principles and operations,” Nature Neuroscience, vol. 15, no. 4, pp. 511–517, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Lisman, “The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme,” Hippocampus, vol. 15, no. 7, pp. 913–922, 2005. View at Publisher · View at Google Scholar · View at Scopus
  36. H. Luo and D. Poeppel, “Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex,” Neuron, vol. 54, no. 6, pp. 1001–1010, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. M. A. Montemurro, M. J. Rasch, Y. Murayama, N. K. Logothetis, and S. Panzeri, “Phase-of-firing coding of natural visual stimuli in primary visual cortex,” Current Biology, vol. 18, no. 5, pp. 375–380, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. T. M. Elliott and F. E. Theunissen, “The modulation transfer function for speech intelligibility,” PLoS Computational Biology, vol. 5, no. 3, Article ID e1000302, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. C. Chandrasekaran, H. K. Turesson, C. H. Brown, and A. A. Ghazanfar, “The influence of natural scene dynamics on auditory cortical activity,” The Journal of Neuroscience, vol. 30, no. 42, pp. 13919–13931, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Gross, N. Hoogenboom, G. Thut et al., “Speech rhythms and multiplexed oscillatory sensory coding in the human brain,” PLoS Biology, vol. 11, Article ID e1001752, 2013. View at Google Scholar
  41. F. D. Szymanski, N. C. Rabinowitz, C. Magri, S. Panzeri, and J. W. H. Schnupp, “The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex,” Journal of Neuroscience, vol. 31, no. 44, pp. 15787–15801, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. A. Mazzoni, S. Panzeri, N. K. Logothetis, and N. Brunel, “Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons,” PLoS Computational Biology, vol. 4, no. 12, Article ID e1000239, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. A. Mazzoni, K. Whittingstall, N. Brunel, N. K. Logothetis, and S. Panzeri, “Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model,” NeuroImage, vol. 52, no. 3, pp. 956–972, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Cavallari, S. Panzeri, and A. Mazzoni, “Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks,” Frontiers in Neural Circuits, vol. 8, article 12, 2014. View at Publisher · View at Google Scholar
  45. D. W. Dong and J. J. Atick, “Statistics of natural time-varying images,” Network: Computation in Neural Systems, vol. 6, pp. 345–358, 1995. View at Publisher · View at Google Scholar · View at Scopus
  46. H. Attias and C. Schreiner, “Temporal low-order statistics of natural sounds,” in Advances in Neural Information Processing Systems, pp. 27–33, MIT Press, Cambridge, Mass, USA, 1997. View at Google Scholar
  47. A. T. Schaefer, K. Angelo, H. Spors, and T. W. Margrie, “Neuronal oscillations enhance stimulus discrimination by ensuring action potential precision,” PLoS Biology, vol. 4, no. 6, article e163, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Kwag, D. McLelland, and O. Paulsen, “Phase of firing as a local window for efficient neuronal computation: tonic and phasic mechanisms in the control of theta spike phase,” Frontiers in Human Neuroscience, vol. 5, article 3, 2011. View at Publisher · View at Google Scholar
  49. A. Arieli, D. Shoham, R. Hildesheim, and A. Grinvald, “Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex,” Journal of Neurophysiology, vol. 73, no. 5, pp. 2072–2093, 1995. View at Google Scholar · View at Scopus
  50. J. B. M. Goense and N. K. Logothetis, “Neurophysiology of the BOLD fMRI signal in awake monkeys,” Current Biology, vol. 18, no. 9, pp. 631–640, 2008. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Okun, A. Naim, and I. Lampl, “The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats,” Journal of Neuroscience, vol. 30, no. 12, pp. 4440–4448, 2010. View at Publisher · View at Google Scholar · View at Scopus
  52. A. Alenda, M. Molano-Mazón, S. Panzeri, and M. Maravall, “Sensory input drives multiple intracellular information streams in somatosensory cortex,” Journal of Neuroscience, vol. 30, no. 32, pp. 10872–10884, 2010. View at Publisher · View at Google Scholar · View at Scopus
  53. V. Braintenberg and A. Schuetz, Cortex: Statistics and Geometry of Neuronal Connectivity, Springer, Berlin, Germany, 1998.
  54. J. D. Fitzgerald, R. J. Rowekamp, L. C. Sincich, and T. O. Sharpee, “Second order dimensionality reduction using minimum and maximum mutual information models,” PLoS Computational Biology, vol. 7, no. 10, Article ID e1002249, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  55. R. A. Ince, S. Panzeri, and C. Kayser, “Neural codes formed by small and temporally precise populations in auditory cortex,” The Journal of Neuroscience, vol. 33, no. 46, pp. 18277–18287, 2013. View at Google Scholar
  56. F. Montani, R. A. Ince, R. Senatore, E. Arabzadeh, M. E. Diamond, and S. Panzeri, “The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex,” Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1901, pp. 3297–3310, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  57. D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999. View at Publisher · View at Google Scholar · View at Scopus
  58. I. Delis, S. Panzeri, T. Pozzo, and B. Berret, “A unifying model of concurrent spatial and temporal modularity in muscle activity,” Journal of Neurophysiology, vol. 111, pp. 675–693, 2014. View at Google Scholar
  59. J. H. Macke, P. Berens, A. S. Ecker, A. S. Tolias, and M. Bethge, “Generating spike trains with specified correlation coefficients,” Neural Computation, vol. 21, no. 2, pp. 397–423, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  60. A. S. Ecker, P. Berens, A. S. Tolias, and M. Bethge, “The effect of noise correlations in populations of diversely tuned neurons,” The Journal of Neuroscience, vol. 31, no. 40, pp. 14272–14283, 2011. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Onken, V. Dragoi, and K. Obermayer, “A maximum entropy test for evaluating higher-order correlations in spike counts,” PLoS Computational Biology, vol. 8, no. 6, Article ID e1002539, 12 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  62. A. Onken, S. Grunewalder, M. H. J. Munk, and K. Obermayer, “Analyzing short-term noise dependencies of spike-counts in Macaque prefrontal cortex using copulas and the flashlight transformation,” PLoS Computational Biology, vol. 5, no. 11, Article ID e1000577, e1000577, 13 pages, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  63. B. Staude, S. Rotter, and S. Grün, “CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains,” Journal of Computational Neuroscience, vol. 29, no. 1-2, pp. 327–350, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  64. M. N. Shadlen and J. A. Movshon, “Synchrony unbound: a critical evaluation of the temporal binding hypothesis,” Neuron, vol. 24, no. 1, pp. 67–77, 111–125, 1999. View at Publisher · View at Google Scholar · View at Scopus
  65. C. von der Malsburg, “The what and why of binding: the modeler's perspective,” Neuron, vol. 24, no. 1, pp. 95–104, 1999. View at Publisher · View at Google Scholar · View at Scopus
  66. S. H. Nirenberg and J. D. Victor, “Analyzing the activity of large populations of neurons: how tractable is the problem?” Current Opinion in Neurobiology, vol. 17, no. 4, pp. 397–400, 2007. View at Publisher · View at Google Scholar · View at Scopus
  67. O. Shriki, A. Kohn, and M. Shamir, “Fast coding of orientation in primary visual cortex,” PLoS Computational Biology, vol. 8, no. 6, Article ID e1002536, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  68. T. Masquelier, “Neural variability, or lack thereof,” Frontiers in Computational Neuroscience, vol. 7, 2013. View at Publisher · View at Google Scholar · View at Scopus
  69. S. Furukawa, L. Xu, and J. C. Middlebrooks, “Coding of sound-source location by ensembles of cortical neurons,” Journal of Neuroscience, vol. 20, no. 3, pp. 1216–1228, 2000. View at Google Scholar · View at Scopus
  70. S. Junek, E. Kludt, F. Wolf, and D. Schild, “Olfactory coding with patterns of response latencies,” Neuron, vol. 67, no. 5, pp. 872–884, 2010. View at Publisher · View at Google Scholar · View at Scopus
  71. A. Kumar, S. Rotter, and A. Aertsen, “Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding,” Nature Reviews Neuroscience, vol. 11, no. 9, pp. 615–627, 2010. View at Publisher · View at Google Scholar · View at Scopus
  72. M. V. Tsodyks and H. Markram, “The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 2, pp. 719–723, 1997. View at Publisher · View at Google Scholar · View at Scopus
  73. R. Gütig and H. Sompolinsky, “The tempotron: a neuron that learns spike timing-based decisions,” Nature Neuroscience, vol. 9, no. 3, pp. 420–428, 2006. View at Publisher · View at Google Scholar · View at Scopus
  74. T. Masquelier, R. Guyonneau, and S. J. Thorpe, “Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains,” PLoS ONE, vol. 3, no. 1, Article ID e1377, 2008. View at Publisher · View at Google Scholar · View at Scopus
  75. A. Luczak, P. Bartho, and K. D. Harris, “Gating of sensory input by spontaneous cortical activity,” Journal of Neuroscience, vol. 33, no. 4, pp. 1684–1695, 2013. View at Publisher · View at Google Scholar · View at Scopus
  76. T. Masquelier, E. Hugues, G. Deco, and S. J. Thorpe, “Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme,” Journal of Neuroscience, vol. 29, no. 43, pp. 13484–13493, 2009. View at Publisher · View at Google Scholar · View at Scopus
  77. D. V. Buonomano and W. Maass, “State-dependent computations: spatiotemporal processing in cortical networks,” Nature Reviews Neuroscience, vol. 10, no. 2, pp. 113–125, 2009. View at Publisher · View at Google Scholar · View at Scopus