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

  • Authors:

  • M. P. Bonomini;J. M. Ferrández;J. Rueda;E. Fernández

  • Affiliations:

  • Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, and CIBER-BBN,;Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, and Dpto. Electrónica, Tecnología de Computadoras, Univ. Politécnica de Cartagena,;Dpto. de Histología y Anatomía, Universidad Miguel Hernández, Alicante,;Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, and CIBER-BBN,

  • Venue:
  • IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
  • Year:
  • 2009

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Abstract

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.