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تسجيل مستخدم جديدDifferential response of the retinal neural code with respect to the sparseness of natural images
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Cesar Ravello
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Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. To investigate the role of this sparseness in the efficiency of the neural code, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we tested the impact of this sparseness parameter on the firing pattern observed in a population of retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon degus. These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images. These results suggest that the code represented in the spike pattern of ganglion cells may adapt to this aspect of the statistics of natural images.
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A central challenge in neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission
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Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. We designed a sparse codin
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