Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Applications of Information Theory to Analysis of Neural Data

  • Simon R. Schultz
  • Robin A. A. Ince
  • Stefano Panzeri
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_280-1

Definition

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information-theoretic quantities are commonly used in neuroscience – (see entry “Definitions of Information-Theoretic Quantities”). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.

Detailed Description

Informati...

Keywords

Manifold Trial Basis 
This is a preview of subscription content, log in to check access

Notes

Acknowledgements

Research is supported by the SI-CODE (FET-Open, FP7-284533) project and by the ABC and NETT (People Programme Marie Curie Actions PITN-GA-2011-290011 and PITN-GA-2011-289146) projects of the European Union’s Seventh Framework Programme FP7 2007–2013.

References

  1. Adrian ED (1928) The basis of sensation. Norton, New York. http://psycnet.apa.org/psycinfo/1928-01753-000. Accessed 17 Jan 2014
  2. Belitski A, Gretton A, Magri C, Marayama Y, Montemurro MA, Logothetis NK, Panzeri S (2008) Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. J Neurosci 28:5696–5709PubMedCrossRefGoogle Scholar
  3. Buzsáki G, Anastassiou CA, Koch C (2012) The origin of extracellular fields and currents – EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13:407–420PubMedCrossRefGoogle Scholar
  4. Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, Schreiter ER, Kerr RA, Orger MB, Jayaraman V, Looger LL, Svoboda K, Kim DS (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300PubMedCentralPubMedCrossRefGoogle Scholar
  5. Denk W, Strickler JH, Webb WW (1990) Two-photon laser scanning fluorescence microscopy. Science 248:73–76PubMedCrossRefGoogle Scholar
  6. Denk W, Delaney KR, Gelperin A, Kleinfeld D, Strowbridge BW, Tank DW, Yuste R (1994) Anatomical and functional imaging of neurons using 2-photon laser scanning microscopy. J Neurosci Methods 54:151–162PubMedCrossRefGoogle Scholar
  7. Einevoll GT, Kayser C, Logothetis NK, Panzeri S (2013) Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat Rev Neurosci 14:770–785PubMedCrossRefGoogle Scholar
  8. Gan JQ (2006) Feature dimensionality reduction by manifold learning in brain-computer interface design. In: Proceedings of the 3rd international workshop on brain-computer interfaces, Graz, Austria, pp 28–29. http://cswww.essex.ac.uk/Research/BCIs/BCI06_GAN1.pdf. Accessed 17 Jan 2014
  9. Ince RAA, Mazzoni A, Petersen RS, Panzeri S (2010) Open source tools for the information theoretic analysis of neural data. Front Neurosci 4:62–70PubMedCentralPubMedGoogle Scholar
  10. Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69:66138CrossRefGoogle Scholar
  11. Magri C, Whittingstall K, Singh V, Logothetis NK, Panzeri S (2009) A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci 10:81PubMedCentralPubMedCrossRefGoogle Scholar
  12. Magri C, Mazzoni A, Logothetis NK, Panzeri S (2012a) Optimal band separation of extracellular field potentials. J Neurosci Methods 210:66–78PubMedCrossRefGoogle Scholar
  13. Magri C, Schridde U, Murayama Y, Panzeri S, Logothetis NK (2012b) The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies. J Neurosci 32:1395–1407PubMedCrossRefGoogle Scholar
  14. Montemurro MA, Panzeri S, Maravall M, Alenda A, Bale MR, Brambilla M, Petersen RS (2007) Role of precise spike timing in coding of dynamic vibrissa stimuli in somatosensory thalamus. J Neurophysiol 98:1871–1882PubMedCrossRefGoogle Scholar
  15. Oñativia J, Schultz SR, Dragotti PL (2013) A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging. J Neural Eng 10:046017PubMedCrossRefGoogle Scholar
  16. Panzeri S, Petersen RS, Schultz SR, Lebedev M, Diamond ME (2001) The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron 29:769–777PubMedCrossRefGoogle Scholar
  17. Quian Quiroga R, Panzeri S (2009) Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci 10:173–185PubMedCrossRefGoogle Scholar
  18. Rieke F, Bialek W, Warland D, de Ruyter van Steveninck RR (1997) Spikes: exploring the neural code. Bradford BookGoogle Scholar
  19. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326PubMedCrossRefGoogle Scholar
  20. Ruyter D, van Steveninck RR, Lewen GD, Strong SP, Koberle R, Bialek W (1997) Reproducibility and variability in neural spike trains. Science 275:1805–1808CrossRefGoogle Scholar
  21. Schultz SR, Kitamura K, Post-Uiterweer A, Krupic J, Häusser M (2009) Spatial pattern coding of sensory information by climbing fiber-evoked calcium signals in networks of neighboring cerebellar purkinje cells. J Neurosci 29:8005–8015PubMedCrossRefGoogle Scholar
  22. Seung HS, Lee DD (2000) The manifold ways of perception. Science 290:2268–2269PubMedCrossRefGoogle Scholar
  23. Stosiek C, Garaschuk O, Holthoff K, Konnerth A (2003) In vivo two-photon calcium imaging of neuronal networks. Proc Natl Acad Sci U S A 100:7319–7324PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Simon R. Schultz
    • 1
  • Robin A. A. Ince
    • 2
  • Stefano Panzeri
    • 2
    • 3
  1. 1.Department of BioengineeringImperial College LondonSouth KensingtonUK
  2. 2.Institute of Neuroscience and PsychologyUniversity of GlasgowGlasgowUK
  3. 3.Center for Neuroscience and Cognitive SystemsItalian Institute of TechnologyRovereto (Tn)Italy