Functional Identification of Retinal Ganglion Cells Based on Neural Population Responses

  • M. P. Bonomini
  • J. M. Ferrández
  • E. Fernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4527)

Abstract

The issue of classification has long been a central topic in the analysis of multielectrode data, either for spike sorting or for getting insight into interactions among ensembles of neurons. Related to coding, many multivariate statistical techniques such as linear discriminant analysis (LDA) or artificial neural networks (ANN) have been used for dealing with the classification problem providing very similar performances. This is, there is no method that stands out from others and the right decision about which one to use is mainly depending on the particular cases demands. In this paper, we found groups of rabbit ganglion cells with distinguishable coding performances by means of a simple based on behaviour method. The method consisted of creating population subsets based on the autocorrelograms of the cells and grouping them according to a minimal Euclidian distance. These subpopulations shared functional properties and may be used for functional identification of the subgroups. Information theory (IT) has been used to quantify the coding capability of every subpopulation. It has been described that all cells that belonged to a certain subpopulation showed very small variances in the information they conveyed while these values were significantly different across subpopulations, suggesting that the functional separation worked around the capacity of each cell to code different stimuli. In addition, the overall informational ability of each of the generated subpopulations kept similar. This trend was present for an increasing number of classes until a critical value was reached, proposing a natural value for functional classes.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Migliore, M., Shepherd, G.: Opinion: An integrated approach to classifying neuronal phenotypes. Nat. Rev. Neurosci. 6(10), 810–818 (2005)CrossRefGoogle Scholar
  2. 2.
    Diaz, E.: A functional genomics guide to the galaxy of neuronal cell types. Nat. Neurosci. 9(1), 99–107 (2006)CrossRefGoogle Scholar
  3. 3.
    Xia, Y.: Knowledge-based classification of neuronal fibers in entire brain. In: Med. Image Comput. Assist. Interv. Int. Conf.Google Scholar
  4. 4.
    Costa, L., Velte, T.: Automatic characterization and classification of ganglion cells from the salamander retina. J. Comp. Neurol. 404(1), 33–51 (1999)CrossRefGoogle Scholar
  5. 5.
    Ammermuller, J., Kolb, H.: The organization of the turtle inner retina. I. ON- and OFF-center pathways. J. Comp. Neurol. 358(1), 1–34 (1995)CrossRefGoogle Scholar
  6. 6.
    Ammermuller, J., Weiler, R., Perlman, I.: Short-term effects of dopamine on photoreceptors, luminosity- and chromaticity-horizontal cells in the turtle retina. Vis. Neurosci. 12(3), 403–412 (1995)CrossRefGoogle Scholar
  7. 7.
    Fitzhugh, R.: A Statistical Analyzer for Optic Nerve Messages. J. Gen. Phyosiol. 41, 675–692 (1958)CrossRefGoogle Scholar
  8. 8.
    Rieke, F., et al.: Spikes: Exploring the Neural Code. MIT Press, Cambridge (1997)Google Scholar
  9. 9.
    Golledge, H.D.R., et al.: Correlations, feature-binding and population coding in primary visual cortex. Neuroreport 14(7), 1045–1050 (2003)CrossRefGoogle Scholar
  10. 10.
    Warland, D., Reinagel, P., Meister, M.: Decoding Visual Information from a Population of Retinal Ganglion Cells. J. Neurophysiol. 78, 2336–2350 (1997)Google Scholar
  11. 11.
    Fernández, E., et al.: Population Coding in spike trains of sinultaneosly recorded retinal ganglion cells Information. Brain Res. 887, 222–229 (2000)CrossRefGoogle Scholar
  12. 12.
    Ferrández, J., et al.: A Neural Network Approach for the Analysis of Multineural Recordings in Retinal Ganglion Cells: Towards Population Encoding. In: Mira, J., et al. (eds.) IWANN 1999. LNCS, vol. 1607, pp. 289–298. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. 13.
    Normann, R., et al.: High-resolution spatio-temporal mapping of visual pathways using multi-electrode arrays. Vision Res. 41, 1261–1275 (2001)CrossRefGoogle Scholar
  14. 14.
    Ortega, G., et al.: Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings. J. Neurosci. Meth. 133, 135–141 (2004)CrossRefGoogle Scholar
  15. 15.
    Shoham, S., Fellows, M., Normann, R.: Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Meth. 127, 111–122 (2003)CrossRefGoogle Scholar
  16. 16.
    Bongard, M., Micol, D., Fernández, E.: Nev2lkit: a tool for handling neuronal event files, http://nev2lkit,sourceforge,net/Google Scholar
  17. 17.
    Bonomini, M.P., Ferrández, J.M., Bolea, J.A., Fernández, E.: RDATA-MEANS: An open source tool for the classification and management of neural ensemble recordings. J. Neurosci. Meth. 148, 137–146 (2005)CrossRefGoogle Scholar
  18. 18.
    Shannon, C.: A Mathematical Theory of Communication. Bell sys. Tech. 27, 379–423 (1948)MathSciNetMATHGoogle Scholar
  19. 19.
    Borst, A., Theunissen, F.: Information Theory and Neural Coding. Nature Neurosci. 2(11), 947–957 (1999)CrossRefGoogle Scholar
  20. 20.
    Amigo, J.M., et al.: On the number of states of the neuronal sources. Biosystems 68(1), 57–66 (2003)CrossRefGoogle Scholar
  21. 21.
    Panzeri, S., Pola, G., Petersen, R.S.: Coding of sensory signals by neuronal populations: the role of correlated activity. Neuroscientist 9(3), 175–180 (2003)CrossRefGoogle Scholar
  22. 22.
    Pola, G., et al.: An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network 14(1), 35–60 (2003)CrossRefGoogle Scholar
  23. 23.
    McClelland, J., Rumelhart, D.: Explorations in Parallel Distributed Processing. MIT Press, Cambridge (1986)Google Scholar
  24. 24.
    Kang, K., Shapley, R.M., Sompolinsky, H.: Information tuning of populations of neurons in primary visual cortex. J. Neurosci. 24(15), 3726–3735 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • M. P. Bonomini
    • 1
  • J. M. Ferrández
    • 1
    • 2
  • E. Fernández
    • 1
  1. 1.Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante 
  2. 2.Dpto. Electrónica, Tecnología de Computadoras, Univ. Politécnica de Cartagena, CartagenaSpain

Personalised recommendations