Discovering flexible codes for prediction across timescales in the retina.

The retina must encode visual information in a way that supports fast, predictive behavior despite significant processing delays. How this encoding adapts in an ever-changing world, when the temporal statistics of visual input shift, remains an open question. Here we record from populations of retinal ganglion cells in the axolotl as they respond to a stochastic moving bar stimulus across five different temporal correlation scales. Using the information bottleneck (IB) framework, and treating the prediction horizon as a free parameter inferred from the data, we ask what timescale of future motion the retina is optimized to predict under each stimulus condition. We find that the retina adapts its predictive encoding to the changing stimulus statistics: as the time constant of the stimulus dynamics increases, the inferred prediction horizon lengthens. The population shifts toward encoding more velocity information, and motion anticipation grows, all the while maintaining near-optimal prediction efficiency. Population surprise, quantified through a Boltzmann machine model of the retinal response distribution, tracks stimulus surprise under the inferred optimal compression. This connects the retina's reversal response to efficient predictive encoding. These results show that retinal population codes flexibly adjust their predictive timescale to the temporal structure of their inputs. More broadly, they demonstrate that the IB framework can be used to discover, not just test for, computational objectives in sensory populations.