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2 Publications

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    09/17/20 | Hindbrain double-negative feedback mediates palatability-guided food and water consumption.
    Gong R, Xu S, Hermundstad A, Yu Y, Sternson SM
    Cell. 2020 Sep 17;182(6):1589-1605. doi: 10.1016/j.cell.2020.07.031

    Hunger and thirst have distinct goals but control similar ingestive behaviors, and little is known about neural processes that are shared between these behavioral states. We identify glutamatergic neurons in the peri-locus coeruleus (periLC neurons) as a polysynaptic convergence node from separate energy-sensitive and hydration-sensitive cell populations. We develop methods for stable hindbrain calcium imaging in free-moving mice, which show that periLC neurons are tuned to ingestive behaviors and respond similarly to food or water consumption. PeriLC neurons are scalably inhibited by palatability and homeostatic need during consumption. Inhibition of periLC neurons is rewarding and increases consumption by enhancing palatability and prolonging ingestion duration. These properties comprise a double-negative feedback relationship that sustains food or water consumption without affecting food- or water-seeking. PeriLC neurons are a hub between hunger and thirst that specifically controls motivation for food and water ingestion, which is a factor that contributes to hedonic overeating and obesity.

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    08/03/20 | Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.
    Tesileanu T, Conte MM, Briguglio JJ, Hermundstad AM, Victor JD, Balasubramanian V
    eLife. 2020 Aug 3;9:. doi: 10.7554/eLife.54347

    Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error < 0:13).

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