BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//MindCORE//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
UID:1724-0
SUMMARY:CNI seminar: Victor Barranca
DTSTART:20190319T153000Z
DTEND:20190319T163000Z
DTSTAMP:20181217T164906Z
LAST-MODIFIED:20190221T201222Z
SEQUENCE:0
LOCATION:Barchi Library (140 John Morgan Building), 3620 Hamilton Walk, Philadelphia, PA 19104
DESCRIPTION:Victor Barranca\nDepartment of Mathematics and Statistics\nSwarthmore College\n\n \n\nReconstruction of Sparse Connectivity and Stimuli in Neuronal Networks Using Compressive Sensing of Network Dynamics\n\n \n\nSparsity is a fundamental characteristic of numerous biological\, social\, and technological networks. Neuronal network connectivity demonstrates sparsity on multiple spatial scales and natural stimuli typically also possess sparse representations in appropriate domains. In this talk\, we address the role of sparsity in the efficient encoding of network structure and inputs through nonlinear neuronal network dynamics. We develop a theoretical framework for reconstructing sparse network data by leveraging compressive sensing theory and the linearity of input-output mappings commonly underlying neuronal dynamics. Addressing the theoretical and experimental challenges in measuring structural network connectivity\, we reconstruct model neuronal network connections using the evoked dynamics in response to a small ensemble of random stimuli. Using the reconstructed connectivity matrix\, we then accurately recover detailed network inputs distinct from the random input ensemble. Analyzing several receptive field models\, we investigate how the accuracy of input reconstructions depends on the network architecture\, and demonstrate that the center-surround structure common in the early visual system facilitates marked improvements in natural scene processing well beyond the uniformly-random connectivity typical in compressive sensing theory. However\, we show that the spatial localization inherent in receptive fields combined with information loss introduced by nonlinear neuronal dynamics may underlie deficiencies in processing specific classes of non-natural stimuli\, yielding a novel explanation for the manifestation of certain illusory effects. We expect this talk will provide a new perspective for understanding compressive encoding in sensory systems as well as the structure-function relationship in neuronal networks.\n\n \n\n \n\nA pizza lunch will be served.
END:VEVENT
END:VCALENDAR