DOI: 10.1101/497008Dec 16, 2018Paper

Current models cannot account for V1's specialisation for binocular natural image statistics

BioRxiv : the Preprint Server for Biology
Sid HenriksenBruce G Cumming

Abstract

A long-standing observation about primary visual cortex (V1) is that the stimulus selectivity of neurons can be well explained with a cascade of linear computations followed by a nonlinear rectification stage. This framework remains highly influential in systems neuroscience and has also inspired recent efforts in artificial intelligence. The success of these models include describing the disparity-selectivity of binocular neurons in V1. Some aspects of real neuronal disparity responses are hard to explain with simple linear-nonlinear models, notably the attenuated response of real cells to "anticorrelated" stimuli which violate natural binocular image statistics. General linear-nonlinear models can account for this attenuation, but no one has yet tested whether they quantitatively match the response of real neurons. Here, we exhaustively test this framework using recently developed optimisation techniques. We show that many cells are very poorly characterised by even general linear-nonlinear models. Strikingly, the models can account for neuronal responses to unnatural anticorrelated stimuli as well as to most natural, correlated stimuli. However, the models fail to capture the particularly strong response to binocularly corre...Continue Reading

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