Advertisement

An Optimized Framework to Model Vertebrate Retinas

  • Andrés Olmedo-Payá
  • Antonio Martínez-Álvarez
  • Sergio Cuenca-Asensi
  • Jose M. Ferrández
  • Eduardo Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. Therefore the retina performs spatial, temporal, and chromatic processing on visual information and converts it into a compact ’digital’ format composed of neural impulses. However, how groups of retinal ganglion cells encode a broad range of visual information is still a challenging and unresolved question. The main objective of this work is to design and develop a new functional tool able to describe, simulate and validate custom retina models. The whole system is optimized for visual neuroprosthesis and can be accelerated by using FPGAs, COTS microprocessors or GP-GPU based systems.

Keywords

Artificial retinas visual neuroprostheses retina simulation spiking neurons 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bongard, M., Ferrandez, J.M., Fernandez, E.: The neural concert of vision. Neurocomputing 72, 814–819 (2009)CrossRefGoogle Scholar
  2. 2.
    Fernandez, E., Pelayo, F., Romero, S., Bongard, M., Marin, C., Alfaro, A., Merabet, L.: Development of a cortical visual neuroprosthesis for the blind: the relevance of neuroplasticity. J. Neural Eng. 2, R1–R12 (2005)CrossRefGoogle Scholar
  3. 3.
    Normann, R.A., Greger, B.A., House, P., Romero, S., Pelayo, F., Fernandez, E.: Toward the development of a cortically based visual neuroprosthesis. J. Neural Eng. 6, 1–8 (2009)CrossRefGoogle Scholar
  4. 4.
    NEV 2.0 (Neural Event Format) format specification, http://cyberkineticsinc.com/NEVspc20.pdf
  5. 5.
    Morillas, C.A., Romero, S.F., Martínez, A., Pelayo, F.J., Fernández, E.: A Computational Tool to Test Neuromorphic Encoding Schemes for Visual Neuroprostheses. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 268–316. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Roessler, G., et al.: Implantation and Explantation of a Wireless Epiretinal Retina Implant Device: Observations during the EPIRET3 Prospective Clinical Trial. Investigative Ophthalmology & Visual Science 50, 3003–3008 (2009)CrossRefGoogle Scholar
  7. 7.
    Sousa, L., Tomas, P., Pelayo, F., Martínez, A., Morillas, C.A., Romero, S.: Bioinspired stimulus encode for cortical visual neuroprostheses. In: New Algorithms, Architectures, and Applications for Reconfigurable Computing, ch. 22, pp. 279–290. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Biomedical Technologies –Stim100, http://biomedical-technologies.com
  9. 9.
    Open Computer vision Library, http://opencvlibrary.sourceforge.net
  10. 10.
    Gerstner, W., Kistler, W.: Spiking neuron models. Cambridge Univeristy Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrés Olmedo-Payá
    • 1
  • Antonio Martínez-Álvarez
    • 2
  • Sergio Cuenca-Asensi
    • 2
  • Jose M. Ferrández
    • 3
  • Eduardo Fernández
    • 1
  1. 1.Institute of Bioengineering and CIBER BBNUniversity Miguel HernandezAlicanteSpain
  2. 2.Computer Technology DepartmentUniversity of AlicanteAlicanteSpain
  3. 3.Department of Electronics and Computer TecnologyUniversidad Politécnica de CartagenaSpain

Personalised recommendations