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 focus on a neural circuit (the bulb) where incoming odor inputs are integrated with extensive contextual feedback from areas of the brain involved in multi-sensory association, attention, memory, and decision- making. While most research in olfaction has examined the structure and function of this system through bottom-up modeling and experimental approaches, here we start from normative principles to derive a theory for context-enhanced efficient coding of olfactory spaces. 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 make a number of predictions on how optimal odor representations change with the statistics of olfactory and associated contextual spaces. These predictions will be tested in electrophysiology and behavioral experiments. We also show in a biophysical model that our normative solution can be implemented at the level of the neural circuit through synaptic plasticity. This is a striking result that reconnects our theoretical findings to biologically plausible processes, thus bridging normative and mechanistic levels of analysis. Our theory is generalizable to other sensory systems and establishes a conceptual foundation for studying sensory coding associated with behavior.