March 2012
Volume 53, Issue 14
ARVO Annual Meeting Abstract  |   March 2012
Retinal Feature Extraction From Asynchronous Light Acquisition
Author Affiliations & Notes
  • Henri Lorach
    Institut de la Vision, Universite Pierre et Marie Curie, Paris, France
  • Ryad Benosman
    Institut de la Vision, Universite Pierre et Marie Curie, Paris, France
  • Sio-Hoi Ieng
    Institut de la Vision, Universite Pierre et Marie Curie, Paris, France
  • Jose A. Sahel
    Institut de la Vision, INSERM, Paris, France
  • Serge A. Picaud
    Institut de la Vision, INSERM, Paris, France
  • Footnotes
    Commercial Relationships  Henri Lorach, None; Ryad Benosman, None; Sio-Hoi Ieng, None; Jose A. Sahel, None; Serge A. Picaud, None
  • Footnotes
    Support  Ecole Polytechnique fellowship
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 1939. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Henri Lorach, Ryad Benosman, Sio-Hoi Ieng, Jose A. Sahel, Serge A. Picaud; Retinal Feature Extraction From Asynchronous Light Acquisition. Invest. Ophthalmol. Vis. Sci. 2012;53(14):1939.

      Download citation file:

      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Purpose: : Develop of an asynchronous model of the retina reproducing biological timing accuracy for a majority of ganglion cell types.

Methods: : An asynchronous light sensor was used for light acquisition. Each pixel of this sensor generates an event as soon as the incident light reaches a particular threshold independently of the other pixels. The threshold follows a logarithmic adaptation and mimics photoreceptors gain control. It allows a sparse coding and a reduction of the redundancy present in frame-based acquisition. From this signal, we used a linear/non-linear model with an integrate and fire spike generation mechanism to reproduce retinal spikes trains. We relied on physiological data from the literature to fit the parameters of our model. The stimulus consisted into a 1s square of light sequentially translated through the receptive field of the cells. To probe spiking statistics, we used a spatially uniform random stimulation and quantified the timing accuracy as well as the variability in the number of spikes.

Results: : By using a fast asynchronous light acquisition sensor, we showed that we could reproduce the physiological responses of the majority of ganglion cell types in the rabbit retina (ON beta, OFF beta, ON parasol, OFF parasol, ON delta, OFF delta, ON bistratified, OFF coupled, ON-OFF direction selective and local edge detector). More importantly, we were able to reproduce the millisecond precision critical in retinal coding. The standard deviation of the spike timings ranged from 2ms to 8ms in close agreement with physiological findings. Finally, we quantified the spiking statistics by computing the mean spike count versus its variability. We found a Fano factor of 0.195, compatible with previous observations in mammals.

Conclusions: : We showed that the millisecond precision of retinal spike trains could be reached by using an asynchronous light acquisition device. We therefore provide an alternative to computationally intensive image processing in the design of artificial retinas. In particular, our approach is well suited for real time applications of retinal models as retinal prosthetics and optogenetic rehabilitation in blind patients.

Keywords: retinal connections, networks, circuitry 

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.