VIRTUAL RETINA Software

VIRTUAL RETINA: large-scale simulations of biologically-plausible retinas

A. Wohrer and P. Kornprobst

Abstract

VIRTUAL RETINA allows large-scale simulations of biologically-plausible retinas, with customizable parameters, and different possible biological features:

  • Spatio-temporal linear filter implementing the basic Center/Surround organization of retinal filtering.
  • Non-linear contrast gain control mechanism providing instantaneous adaptation to the local level of contrast. This stage is modelled through dynamical adaptation conductances in the membranes of bipolar cells; the resulting model reproduces contrast-dependent amplitude and phase non-linearities, as measured in real mammalian retinas by Shapley & Victor 78.
  • Spike generation by one or several layers of ganglion cells paving the visual field. Magnocellular and Parvocellular pathways can be modelled in the same framework according to the parameters chosen. Large-scale simulations can be pursued on up to 100,000 spiking cells.
  • Possibility of a global radial inhomogeneity modeling the foveated organization of mammalian retinas. In this case, the spatial scales of filtering, and the density of spiking cells, both depend on the eccentricity from the center of the retina.
  • Possibility to include a basic microsaccades generator at the input of the retina, to account for fixational eye movements.

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Paper | Source Code

Bibtex

@Article{ wohrer-kornprobst:09,
AUTHOR = {Adrien Wohrer and Pierre Kornprobst},
TITLE = {{Virtual Retina} : A biological retina model and simulator, with contrast gain control},
JOURNAL = {Journal of Computational Neuroscience},
YEAR = {2009},
VOLUME = {26},
NUMBER = {2},
PAGES = {219},
NOTES = {DOI 10.1007/s10827-008-0108-4},
}

Selection of papers using VIRTUAL RETINA

  • Convis: A Toolbox To Fit and Simulate Filter-based Models of Early Visual Processing, Jacob Huth, Timothee Masquelier and Angelo Arleo
  • Microsaccades enable efficient synchrony-based coding in the retina: a simulation study, T. Masquelier, G. Portelli, and P. Kornprobst. Scientific Reports, 6:24086, April 2016.
  • Modelling of a retinal ganglion cell with simple spiking models, P. Vance, S. A. Coleman, D. Kerr, G.P. Das, and T.M. McGinnity, In IEEE Int. Jt. Conf. Neural Networks, pages 1–8, 2015.
  • Evaluating SPAN Incremental Learning for Handwritten Digit Recognition, A. Mohemmed, G. Lu, and N. Kasabov, In Neural Information Processing, pages 670–677. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.
  • Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model, T. Masquelier, Journal of Computational Neuroscience, 32(3):425–441, 2012.
  • Another look at the retina as an image scalar quantizer, K. Masmoudi, M. Antonini, and P. Kornprobst, In Proceedings of the International Symposium on Circuits and Systems (ISCAS), 2010.

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VIRTUAL RETINA is now part of the PRANAS software so that you can use it easily thanks to our friendly Graphical User Interface!

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