Neural processing of natural scenes in the visual cortex

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Abstract The retina and visual cortex represents visual information in the form of a complex set of electrical signals to support visual behavior and memory. Although we have learned a great deal about how simple visual patterns such as striped gratings lead to neural activity in the early visual system, we know little about how natural visual scenes are represented during behavior, and how the active process of gathering visual information through body and eye movements influences this process. Visual processing becomes progressively more complex towards higher levels in the brain. Compared to primates, mice combine strong motor input with visual input at an earlier level in the visual stream, the primary visual cortex. This makes the mouse visual system an accessible to system to understand how natural scenes are represented and influenced by active sensation at a level in the visual system where computational models of the neural code for natural scenes are more tractable. This proposal has two primary goals. First to determine how the neural code changes for natural scenes from the retina to the cortex with an accurate computational model that can be analyzed to determine how specific retinal cell types contribute to cortical activity for ethological computations such as determining motion direction and speed, adaptation and object motion detection. Second, to test alternative theoretically grounded hypotheses as to how motor activity influences the representation of natural scenes, including the subtraction of expected visual stimuli to create a more efficient representation, known as predictive coding, predictive or Bayesian feature detection that adjusts the detection threshold to the prior probability that visual features are present, and simple adaptation to the strength of combined signals to avoid saturation. Using high channel count silicon probes, computational models that combine known biophysical and circuit level properties with interpretable cutting edge machine learning approaches and virtual reality systems, we will gain new insight into visual processing for natural scenes in the early visual system. These results will give a quantitative picture of how the retina and visual cortex function, which will be essential in understand how diseases that affect central visual processing such as amblyopia, strabismus and schizophrenia, and reveal general principles of cortical sensory processing. The computational models established here will also be directly applicable for use in retinal and cortical visual prosthesis systems.