Neural processing of natural scenes in the visual cortex

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.

Grant Number: 5R01EY034566-03
NIH Institute/Center: NIH
Principal Investigator: STEPHEN BACCUS