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A normalized contrast‐encoding model exhibits bright/dark asymmetries similar to early visual neurons

Biological sensory systems share a number of organizing principles. One such principle is the formation of parallel streams. In the visual system, information about bright and dark features is largely conveyed via two separate streams: the ON and OFF pathways. While brightness and darkness can be co...

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Detalles Bibliográficos
Autor principal: Cooper, Emily A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831320/
https://www.ncbi.nlm.nih.gov/pubmed/27044852
http://dx.doi.org/10.14814/phy2.12746
Descripción
Sumario:Biological sensory systems share a number of organizing principles. One such principle is the formation of parallel streams. In the visual system, information about bright and dark features is largely conveyed via two separate streams: the ON and OFF pathways. While brightness and darkness can be considered symmetric and opposite forms of visual contrast, the response properties of cells in the ON and OFF pathways are decidedly asymmetric. Here, we ask whether a simple contrast‐encoding model predicts asymmetries for brights and darks that are similar to the asymmetries found in the ON and OFF pathways. Importantly, this model does not include any explicit differences in how the visual system represents brights and darks, but it does include a common normalization mechanism. The phenomena captured by the model include (1) nonlinear contrast response functions, (2) greater nonlinearities in the responses to darks, and (3) larger responses to dark contrasts. We report a direct, quantitative comparison between these model predictions and previously published electrophysiological measurements from the retina and thalamus (guinea pig and cat, respectively). This work suggests that the simple computation of visual contrast may account for a range of early visual processing nonlinearities. Assessing explicit models of sensory representations is essential for understanding which features of neuronal activity these models can and cannot predict, and for investigating how early computations may reverberate through the sensory pathways.