@article {65675, title = {Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Aug 3}, abstract = {

Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a {\textquoteright}variance is salience{\textquoteright} 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 {\textquoteright}variance is salience{\textquoteright} 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).

}, doi = {10.7554/eLife.54347}, url = {https://elifesciences.org/articles/54347}, author = {Tesileanu, Tiberiu and Conte, Mary M and Briguglio, John J and Hermundstad, Ann M and Victor, Jonathan D and Balasubramanian, Vijay} } @article {65792, title = {The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value.}, journal = {Cell}, volume = {183}, year = {2020}, month = {2020 Oct 29}, pages = {620-35}, abstract = {

Hippocampal activity represents many behaviorally important variables, including context, an animal{\textquoteright}s location within a given environmental context, time, and reward. Using longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a single feature-the field propensity. Each cell{\textquoteright}s propensity governs how many place fields it has per unit space, predicts its reward-related activity, and is preserved across distinct environments and over months. Propensity is broadly distributed-with many low, and some very high, propensity cells-and thus strongly shapes hippocampal representations. This results in a range of spatial codes, from sparse to dense. Propensity varied \~{}10-fold between adjacent cells in salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. Intracellular recordings linked propensity to cell excitability. The stability of each cell{\textquoteright}s propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.09.024}, author = {Lee, Jae Sung and Briguglio, John J and Cohen, Jeremy D and Romani, Sandro and Lee, Albert K} } @article {47703, title = {Variance predicts salience in central sensory processing.}, journal = {eLife}, volume = {3}, year = {2014}, month = {2014}, abstract = {

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime--when sampling limitations constrain performance--efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

}, keywords = {Humans, Sensory Receptor Cells, Visual Cortex, Visual Perception}, issn = {2050-084X}, doi = {10.7554/eLife.03722}, author = {Hermundstad, Ann M and Briguglio, John J and Conte, Mary M and Victor, Jonathan D and Balasubramanian, Vijay and Tka{\v c}ik, Ga{\v s}per} }