A simple spiking retina model for exact video stimulus representation
© Lazar and Pnevmatikakis; licensee BioMed Central Ltd. 2008
Published: 11 July 2008
A computational model for the representation of visual stimuli with a population of spiking neurons is presented. We show that under mild conditions it is possible to faithfully represent an analog video stream into a sequence of spike trains and provide an algorithm that recovers the video input by using only the spike times of the population.
We prove and demonstrate that we can recover the whole video stream based only on the knowledge of the spike times, provided that the size of the neural population is sufficiently big. Increasing the number of neurons to achieve better representation is consistent with basic neurobiological thought .
Although very precise, the responses of visual neurons show some variability between subsequent stimulus repeats, which can be attributed to various noise sources . We examine the effect of noise on our algorithm and show that the reconstruction quality gracefully degrades when white noise is present at the input or at the feedback loop.
This work is supported by NIH grant R01 DC008701-01 and NSF grant CCF-06-35252. EA Pnevmatikakis is also supported by Onassis Public Benefit Foundation.
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