Abstract
The fruit fly’s natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.
Highlights
Sensory processing systems in the brain extract relevant information from stimuli whose amplitude can vary orders of magnitude [1,2,3,4]
We address the above issues by modeling the photoreceptor/amacrine cells layer of the fruit fly as a multi-input multi-output (MIMO) feedforward and feedback temporal and spatio-temporal divisive normalization processor (DNP)
5 Discussion As already mentioned in the Introduction, the photoreceptor/amacrine cell layer of the early vision system of the fruit fly rapidly adapts to visual stimuli whose intensity and contrast vary orders of magnitude both in space and time
Summary
Sensory processing systems in the brain extract relevant information from stimuli whose amplitude can vary orders of magnitude [1,2,3,4]. We address the above issues by modeling the photoreceptor/amacrine cells layer of the fruit fly as a multi-input multi-output (MIMO) feedforward and feedback temporal and spatio-temporal divisive normalization processor (DNP). Remark 4 By exploiting the structure of low-rank second-order Volterra kernels, Algorithm 1 provides a tractable solution to the identification of the components of the divisive normalization processor. The output of the photoreceptor DNP model when stimulated by the other 190 seconds of Figure 4 Example of identification of a divisive normalization model. Given the spatio-temporal divisive normalization processor depicted, we are interested in identifying all the filters from input and output observations. We formulate an optimization problem, which achieves such identification, with high fidelity and with a relatively small number of measurements
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