Optimal prediction with resource constraints using the information bottleneck

Responding to stimuli requires that organisms encode information about the external world. Not all parts of the signal are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position coordinates prove more predictive. We identify the properties of global, transferrable strategies for time-varying stimuli. For non-Markovian dynamics we explore the role of long-term memory of the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories, revealing a very different prediction strategy from motion prediction.