Coarse-graining facilitates generalization in populations of retinal ganglion cells

The output of the retina contains all of the information the brain encodes about the visual world. The joint probability distribution of these outputs, retinal ganglion cells (RGCs), is known to change with the statistics of the scene driving retinal activity. This creates a significant challenge in creating generative models of population activity that generalize to new types of stimuli. We build on an information-theoretic coarse-graining proposed by Ramirez and Bialek to take the population of neurons from an exponential number of states to a linear number of states. Using data from RGCs in the larval salamander retina in response to a variety of natural moving scenes, we test how well coarse-grained representations generalize. We find that trial-averaging significantly improves generalization to other natural movies. These coarse-grainings can be input to a Generalized Linear Model (GLM), an interpretable, generative model of retinal activity. With this input, the GLM performs well at recapitulating retinal response and generalizes well to novel stimulus statistics, including from spatial white noise checkerboards to natural movies. This approach may be helpful in modeling other areas of the brain and has implications both for basic neuroscience and machine learning.