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Creators/Authors contains: "Wang, B"

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  1. ; ; ( , Journal of computational biology)
    Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task.more »We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them.« less
    Free, publicly-accessible full text available January 1, 2023
  2. ; ; ; ( , Catalysis reviews science and engineering)
    Water is ubiquitous in many thermal treatments and reaction conditions involving zeolite catalysts, but the potential impacts are complex. The different types of water interaction with zeolites have profound consequences in the stability, structure/ composition, and reactivity of these important catalysts. This review analyzes the current knowledge about the mechanistic aspects of water adsorption and nucleation on zeolites surfaces and the concomitant role of zeolite defects, cations and extra framework species. Examples of experimental and computational studies of water interaction with zeolites of varying Si/Al ratios, topologies, and level of silanol defects are reviewed and analyzed. The different steps associatedmore »with the process of steaming, including the Al-O-Si bond hydrolysis and subsequent structural modifications, such as dealumination, mesopore formation, and amorphization, are evaluated in light of recent DFT calculations, as well as SS NMR and other spectroscopic studies. Differences between the mechanisms of water attack of the zeolite in vapor or liquid phase are highlighted and explained, as well as the effect of hydrophobic/hydrophilic properties of the zeolite walls. In parallel, the various roles of water as modifier of reactivity are reviewed and discussed, both for plain zeolites as well as rare-earth or phosphorous-modified materials.« less
    Free, publicly-accessible full text available August 1, 2022
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available May 1, 2023
  4. ; ; ; ; ; ; ; ; ( , Physical review)
    Sc 3 Mn 3 Al 7 Si 5 is a rare example of a correlated metal in which the Mn moments form a kagome lattice. The absence of magnetic ordering to the lowest temperatures suggests that geometrical frustration of magnetic interactions may lead to strong magnetic fluctuations. We have performed inelastic neutron scattering measurements on Sc 3 Mn 3 Al 7 Si 5 , finding that phonon scattering dominates for energies from ∼20–50 meV. These results are in good agreement with ab initio calculations of the phonon dispersions and densities of states, and as well reproduce the measured specific heat.more »A weak magnetic signal was detected at energies less than ∼10 meV, present only at the lowest temperatures. The magnetic signal is broad and quasielastic, as expected for metallic paramagnets« less
    Free, publicly-accessible full text available October 12, 2022
  5. ; ; ; ; ; ( , IEEE Conference on Computer Vision and Pattern Recognition)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ( , Proceedings of the National Academy of Sciences of the United States of America)
    Bacterial cells can self-organize into structured communities at fluid-fluid interfaces. These soft, living materials composed of cells and extracellular matrix are called pellicles. Cells residing in pellicles garner group-level survival advantages such as increased antibiotic resistance. The dynamics of pellicle formation and, more generally, how complex morphologies arise from active biomaterials confined at interfaces are not well understood. Here, using Vibrio cholerae as our model organism, a custom-built adaptive stereo microscope, fluorescence imaging, mechanical theory, and simulations, we report a fractal wrinkling morphogenesis program that differs radically from the well-known coalescence of wrinkles into folds that occurs in passive thinmore »films at fluid-fluid interfaces. Four stages occur: growth of founding colonies, onset of primary wrinkles, development of secondary curved ridge instabilities, and finally the emergence of a cascade of finer structures with fractal-like scaling in wavelength. The time evolution of pellicle formation depends on the initial heterogeneity of the film microstructure. Changing the starting bacterial seeding density produces three variations in the sequence of morphogenic stages, which we term the bypass, crystalline, and incomplete modes. Despite these global architectural transitions, individual microcolonies remain spatially segregated, and thus the community maintains spatial and genetic heterogeneity. Our results suggest that the memory of the original microstructure is critical in setting the morphogenic dynamics of a pellicle as an active biomaterial.« less
    Accepted Manuscript Full Text Available
  7. ( , Proceedings of 35th AAAI Conf. on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  8. ; ; ( , 8th Workshop on Biological Distributed Algorithms (BDA))
    Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization ismore »a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.« less
    Accepted Manuscript Full Text Available
  9. ; ; ( , 8th Workshop on Biological Distributed Algorithms (BDA))
    Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization ismore »a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.« less
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ; ( , Methods in cell biology)
    Guichard, P. ; Hamel, V. (Ed.)
    This chapter describes two mechanical expansion microscopy methods with accompanying step-by-step protocols. The first method, mechanically resolved expansion microscopy, uses non-uniform expansion of partially digested samples to provide the imaging contrast that resolves local mechanical properties. Examining bacterial cell wall with this method, we are able to distinguish bacterial species in mixed populations based on their distinct cell wall rigidity and detect cell wall damage caused by various physiological and chemical perturbations. The second method is mechanically locked expansion microscopy, in which we use a mechanically stable gel network to prevent the original polyacrylate network from shrinking in ionic buffers.more »This method allows us to use anti-photobleaching buffers in expansion microscopy, enabling detection of novel ultra-structures under the optical diffraction limit through super-resolution single molecule localization microscopy on bacterial cells and whole-mount immunofluorescence imaging in thick animal tissues. We also discuss potential applications and assess future directions.« less
    Full Text Available