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Williamson, Ross

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Williamson

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Williamson, Ross
Author's Publications: search.filters.isAuthorOfPublication.329a7ff9-7790-4e1a-bd5f-2ef704f2bd54

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    Sensory overamplification in layer 5 auditory corticofugal projection neurons following cochlear nerve synaptic damage
    (Nature Publishing Group UK, 2018) Asokan, Meenakshi M.; Williamson, Ross; Hancock, Kenneth; Polley, Daniel
    Layer 5 (L5) cortical projection neurons innervate far-ranging brain areas to coordinate integrative sensory processing and adaptive behaviors. Here, we characterize a plasticity in L5 auditory cortex (ACtx) neurons that innervate the inferior colliculus (IC), thalamus, lateral amygdala and striatum. We track daily changes in sound processing using chronic widefield calcium imaging of L5 axon terminals on the dorsal cap of the IC in awake, adult mice. Sound level growth functions at the level of the auditory nerve and corticocollicular axon terminals are both strongly depressed hours after noise-induced damage of cochlear afferent synapses. Corticocollicular response gain rebounded above baseline levels by the following day and remained elevated for several weeks despite a persistent reduction in auditory nerve input. Sustained potentiation of excitatory ACtx projection neurons that innervate multiple limbic and subcortical auditory centers may underlie hyperexcitability and aberrant functional coupling of distributed brain networks in tinnitus and hyperacusis.
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    Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation
    (Frontiers Media S.A., 2017) Meyer, Arne F.; Williamson, Ross; Linden, Jennifer F.; Sahani, Maneesh
    Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available.