Modelling Retinal Feature Detection With Deep Belief Networks In A Simulated Environment
نویسندگان
چکیده
Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper, we address this issue and demonstrate how the retina’s inherent multi-layered structure lends itself naturally for modelling with deep networks. We introduce a method for simulating the retinal photoreceptor input and show the efficacy of deep networks in learning feature detectors similar to retinal ganglion cells. We thereby demonstrate the potential of deep belief networks for modelling the earliest stages of visual processing.
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