The neural mechanisms that lead to sparse coding within the midbrain of weakly electric fish

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Understanding how natural stimuli are encoded by peripheral sensory neurons and subsequently decoded by higher structures, the neural code, is a central problem in neuroscience. The long-term goals of the proposed research program are to understand the cellular and network mechanisms that make sensory neurons more selective in their responses to natural sensory input. It is generally agreed that peripheral sensory neurons employ dense neural codes (i.e. codes in which neurons respond to a wide range of stimuli) and that neurons in higher areas employ sparse neural codes (i.e. codes in which neurons respond selectively to the sparsely distributed informative components of natural stimuli). These sparse codes have now been widely observed across animal taxa, including the human hippocampus, monkey visual cortex, locust olfactory system, and songbird forebrain. It is argued that such sparse codes are more efficient because individual neurons detect specific features of sensory input and are metabolically less costly than dense codes. At the same time, recognition of a given stimulus feature (e.g. a particular object, face) requires that neural responses become robust to the sometimes very different inputs associated with this feature (e.g. the same object observed from different viewpoints). I propose to continue this research program by focusing on understanding the mechanisms that make midbrain neurons respond selectively to communication stimuli using the well-characterized electrosensory system of the gymnotiform fish Apteronotus leptorhynchus focusing on the following key issues: 1) Where are dense and sparse coding neurons located in TS? 2) What are the mechanisms that mediate the emergence of a sparse code and how are the tradeoffs between selectivity and feature invariance achieved? I propose to answer these important questions using a combination of in vivo patch clamp recordings, pharmacology, and mathematical modeling, all of which are routinely achieved in my laboratory. This combination has already successfully uncovered the mechanisms that lead to directional selectivity in midbrain neurons and I am confident that it will also successfully uncover the mechanisms that lead to responses selectivity and invariance to communication stimuli. In particular, we will test whether specific membrane conductances are critical in order to achieve response selectivity and feature invariance by using specific blockers as done previously. Further, using a combination of anatomical and physiological data, we will test whether the transformation from a dense code to a sparse code occurs sequentially in the electrosensory midbrain or whether dense and sparse codes co-exist in parallel. It is expected that the uncovered mechanisms will be generic and thus applicable to other systems and species. **