1,322 research outputs found

    UMSL Bulletin 2023-2024

    Get PDF
    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

    Get PDF
    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Lack of orientation specific adaptation to vertically oriented Glass patterns in human visual cortex: an fMRI adaptation investigation

    Get PDF
    The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity

    2017 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Automatic, early color-specific neural responses to object color knowledge

    Get PDF
    Some familiar objects are associated with specific colors, e.g., rubber ducks with yellow. Whether and at what stage neural responses occur to these color associations remain open questions. We recorded frequency-tagged electroencephalogram (EEG) responses to periodic presentations of yellow-associated objects, shown among sequences of non-periodic blue-, red-, and green- associated objects. Both color and grayscale versions of the objects elicited yellow-specific responses, indicating an automatic activation of color knowledge from object shape. Follow-up experiments replicated these effects with green-specific responses, and demonstrated modulated responses for incongruent color/object associations. Importantly, the onset of color-specific responses was as early to grayscale as actually colored stimuli (before 100 ms), the latter additionally eliciting a conventional later response (approximately 140-230 ms) to actual stimulus color. This suggests that the neural representation of familiar objects includes both diagnostic shape and color properties, such that shape can elicit associated color-specific responses before actual color-specific responses occur

    Functional connectivity and dendritic integration of feedback in visual cortex

    Get PDF
    A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing

    Computational Imaging for Phase Retrieval and Biomedical Applications

    Get PDF
    In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges. The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging. The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.</p

    Musiktheorie als interdisziplinĂ€res Fach: 8. Kongress der Gesellschaft fĂŒr Musiktheorie Graz 2008

    Get PDF
    Im Oktober 2008 fand an der UniversitĂ€t fĂŒr Musik und darstellende Kunst Graz (KUG) der 8. Kongress der Gesellschaft fĂŒr Musiktheorie (GMTH) zum Thema »Musiktheorie als interdisziplinĂ€res Fach« statt. Die hier vorgelegten gesammelten BeitrĂ€ge akzentuieren Musiktheorie als multiperspektivische wissenschaftliche Disziplin in den Spannungsfeldern Theorie/Praxis, Kunst/Wissenschaft und Historik/Systematik. Die sechs Kapitel ergrĂŒnden dabei die Grenzbereiche zur Musikgeschichte, MusikĂ€sthetik, zur Praxis musikalischer Interpretation, zur kompositorischen Praxis im 20. und 21. Jahrhundert, zur Ethnomusikologie sowie zur Systematischen Musikwissenschaft. Insgesamt 45 AufsĂ€tze, davon 28 in deutscher, 17 in englischer Sprache, sowie die Dokumentation einer Podiumsdiskussion zeichnen in ihrer Gesamtheit einen höchst lebendigen und gegenwartsbezogenen Diskurs, der eine einzigartige Standortbestimmung des Fachs Musiktheorie bietet.The 8th congress of the Gesellschaft fĂŒr Musiktheorie (GMTH) took place in October 2008 at the University for Music and Dramatic Arts Graz (KUG) on the topic »Music Theory and Interdisciplinarity«. The collected contributions characterize music theory as a multi-faceted scholarly discipline at the intersection of theory/practice, art/science and history/system. The six chapters explore commonalties with music history, music aesthetics, musical performance, compositional practice in twentieth- and twenty-first-century music, ethnomusicology and systematic musicology. A total of 45 essays (28 in German, 17 in English) and the documentation of a panel discussion form a vital discourse informed by contemporaneous issues of research in a broad number of fields, providing a unique overview of music theory today. A comprehensive English summary appears at the beginning of all contributions

    Insect neuroethology of reinforcement learning

    Get PDF
    Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning
    • 

    corecore