On the context-dependent efficient coding of olfactory spaces

Sensory neural representations are modulated by a variety of contextual factors, such as multi-modal cues, stimulus history, novelty, behavioral utility, and internal states. Despite decades of attention in systems neuroscience, many questions persist regarding how sensory codes adapt to these different variables. Here, we study this problem in the olfactory system. We present an integrative approach combining normative theories of context-enhanced efficient coding and mechanistic models of neural circuits to generate predictions that will be tested in electrophysiology and behavioral experiments. Our theory is based on the information-theoretic premise that optimal codes strive to maximize the overall entropy (decodability) of neural representations while minimizing neural costs. A novel feature of our approach consists in incorporating feedback into this framework, which allows us to predict how optimal odor representations depend on top-down contextual signals and their covariance with odor spaces. We also show how normative solutions can be implemented at the level of neural circuits through various forms of plasticity. Our theory is generalizable to other sensory circuits and establishes a conceptual foundation for studying sensory coding associated with behavior.