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

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Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy (IWINAC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5601))

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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.

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Author information

Authors and Affiliations

  1. Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain

    M. P. Bonomini, J. M. Ferrández & E. Fernández

  2. Dpto. Electrónica, Tecnología de Computadoras, Univ. Politécnica de Cartagena, Spain

    J. M. Ferrández

  3. CIBER-BBN, Spain

    M. P. Bonomini & E. Fernández

  4. Dpto. de Histología y Anatomía, Universidad Miguel Hernández, Alicante, Spain

    J. Rueda

Authors
  1. M. P. Bonomini
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  2. J. M. Ferrández
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  3. J. Rueda
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  4. E. Fernández
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Editor information

Editors and Affiliations

  1. Dapartamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, E.T.S. de Ingeniería Informática, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & Félix de la Paz  & 

  2. Departamento de Electrónica, Tecnología de Computadores y Proyectos, Universidad Politécnica de Cartagena, Pl. Hospital, 1, 30201, Cartagena, Spain

    José Manuel Ferrández

  3. Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, E.T.S. de Ingeniería Informática, Juan del Rosal, 16, 28040, Madrid, Spain

    José R. Álvarez

  4. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Pl. Hospital, 1, 30201, Cartagena

    F. Javier Toledo

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Bonomini, M.P., Ferrández, J.M., Rueda, J., Fernández, E. (2009). Analysis of Retinal Ganglion Cells Population Responses Using Information Theory and Artificial Neural Networks: Towards Functional Cell Identification. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_14

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  • DOI: https://doi.org/10.1007/978-3-642-02264-7_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02263-0

  • Online ISBN: 978-3-642-02264-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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