Analysis of Retinal Ganglion Cells Population Responses Using Information Theory and Artificial Neural Networks: Towards Functional Cell Identification

  • M. P. Bonomini
  • J. M. Ferrández
  • J. Rueda
  • E. Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5601)


In this paper, we analyse the retinal population data looking at behaviour. The method is based on creating population subsets using the autocorrelograms of the cells and grouping them according to a minimal Euclidian distance. These subpopulations share functional properties and may be used for data reduction, extracting the relevant information from the code. Information theory (IT) and artificial neural networks (ANNs) have been used to quantify the coding goodness of every subpopulation, showing a strong correlation between both methods. 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.


Mutual Information Retinal Ganglion Cell Spike Train Neural Code Population Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. P. Bonomini
    • 1
    • 3
  • J. M. Ferrández
    • 1
    • 2
  • J. Rueda
    • 4
  • E. Fernández
    • 1
    • 3
  1. 1.Instituto de BioingenieríaUniversidad Miguel HernándezAlicanteSpain
  2. 2.Dpto. Electrónica, Tecnología de ComputadorasUniv. Politécnica de CartagenaSpain
  3. 3.CIBER-BBNSpain
  4. 4.Dpto. de Histología y AnatomíaUniversidad Miguel HernándezAlicanteSpain

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