A computational tool to test neuromorphic encoding schemes for visual neuroprostheses

  • Authors:

  • Christian A. Morillas;Samuel F. Romero;Antonio Martinez;Francisco J. Pelayo;Eduardo Fernández

  • Affiliations:

  • Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Histology and Inst. of Bioengineering, University Miguel Hernández, Alicante, Spain

  • Venue:
  • IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
  • Year:
  • 2005

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Abstract

Recent advances in arrays of microelectrodes open the door to both better understanding of the way the brain works and to the restoration of damaged perceptive and motor functions. In the case of sensorial inputs, direct multi-channel interfacing with the brain for neuro-stimulation requires a computational layer capable of handling the translation from external stimuli into appropriate trains of spikes. The work here presented aims to provide automated and reconfigurable transformation of visual inputs into addresses of microelectrodes in a cortical implant for the blind. The development of neuroprostheses such as this one will contribute to reveal the neural language of the brain for the representation of perceptions, and offers a hope to persons with deep visual impairments. Our system serves as a highly flexible test-bench for almost any kind of retina model, and allows the validation of these models against multi-electrode recordings from experiments with biological retinas. The current version is a PC-based platform, and a compact stand-alone device is under development for the autonomy and portability required in chronic implants. This tool is useful for psychologists, neurophysiologists, and neural engineers as it offers a way to deal with the complexity of multi-channel electrical interfaces for the brain.