@article {68296, title = {Disorder and the Neural Representation of Complex Odors.}, journal = {Frontiers in Computational Neuroscience}, volume = {16}, year = {2022}, month = {2022 Aug 08}, pages = {917786}, abstract = {

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

}, issn = {1662-5188}, doi = {10.3389/fncom.2022.917786}, author = {Krishnamurthy, Kamesh and Hermundstad, Ann M and Mora, Thierry and Walczak, Aleksandra M and Balasubramanian, Vijay} } @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 {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} }