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            "title": "Representation of concurrent stimuli by population activity in visual cortex",
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                    "firstName": "L.",
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            "publicationTitle": "Neuron",
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            "date": "2009",
            "volume": "64",
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            "creatorSummary": "Zuiderbaan et al.",
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            "itemType": "journalArticle",
            "title": "Modeling center-surround configurations in population receptive fields using fMRI",
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                    "creatorType": "author",
                    "firstName": "Wietske",
                    "lastName": "Zuiderbaan"
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                    "firstName": "Ben M",
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                    "firstName": "Serge O",
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            "abstractNote": "Antagonistic center-surround configurations are a central organizational principle of our visual system. In visual cortex, stimulation outside the classical receptive field can decrease neural activity and also decrease functional Magnetic Resonance Imaging (fMRI) signal amplitudes. Decreased fMRI amplitudes below baseline-0% contrast-are often referred to as \"negative\" responses. Using neural model-based fMRI data analyses, we can estimate the region of visual space to which each cortical location responds, i.e., the population receptive field (pRF). Current models of the pRF do not account for a center-surround organization or negative fMRI responses. Here, we extend the pRF model by adding surround suppression. Where the conventional model uses a circular symmetric Gaussian function to describe the pRF, the new model uses a circular symmetric difference-of-Gaussians (DoG) function. The DoG model allows the pRF analysis to capture fMRI signals below baseline and surround suppression. Comparing the fits of the models, an increased variance explained is found for the DoG model. This improvement was predominantly present in V1/2/3 and decreased in later visual areas. The improvement of the fits was particularly striking in the parts of the fMRI signal below baseline. Estimates for the surround size of the pRF show an increase with eccentricity and over visual areas V1/2/3. For the suppression index, which is based on the ratio between the volumes of both Gaussians, we show a decrease over visual areas V1 and V2. Using non-invasive fMRI techniques, this method gives the possibility to examine assumptions about center-surround receptive fields in human subjects.",
            "publicationTitle": "Journal of vision",
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                    "firstName": "Stanley Jose",
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                    "firstName": "Jose-Manuel",
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            "abstractNote": "Visual information is mediated by two major thalamic pathways that signal light decrements (OFF) and increments (ON) in visual scenes, the OFF pathway being faster than the ON. Here, we demonstrate that this OFF temporal advantage is transferred to visual cortex and has a correlate in human perception. OFF-dominated cortical neurons in cats responded ∼3 ms faster to visual stimuli than ON-dominated cortical neurons, and dark-mediated suppression in ON-dominated neurons peaked ∼14 ms faster than light-mediated suppression in OFF-dominated neurons. Consistent with the neuronal differences, human observers were 6–14 ms faster at detecting darks than lights and better at discriminating dark than light flickers. Neuronal and perceptual differences both vanished if backgrounds were biased toward darks. Our results suggest that the cortical OFF pathway is faster than the ON pathway at increasing and suppressing visual responses, and these differences have parallels in the human visual perception of lights and darks.",
            "publicationTitle": "Neuron",
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            "date": "2014",
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                    "firstName": "Misha B.",
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            "abstractNote": "Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.",
            "publicationTitle": "Nature Methods",
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            "date": "2013",
            "volume": "10",
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            "abstractNote": "Object recognition has been a central yet elusive goal of computational vision. For many years, computer performance seemed highly deficient and unable to emulate the basic capabilities of the human recognition system. Over the past decade or so, computer scientists and neuroscientists have developed algorithms and systems—and models of visual cortex—that have come much closer to human performance in visual identification and categorization. In this personal perspective, we discuss the ongoing struggle of visual models to catch up with the visual cortex, identify key reasons for the relatively rapid improvement of artificial systems and models, and identify open problems for computational vision in this domain.",
            "publicationTitle": "Annals of the New York Academy of Sciences",
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            "date": "2013",
            "volume": "1305",
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            "pages": "72-82",
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            "journalAbbreviation": "Ann. N.Y. Acad. Sci.",
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            "url": "http://onlinelibrary.wiley.com/doi/10.1111/nyas.12148/abstract",
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            "shortTitle": "Vision",
            "language": "en",
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            "callNumber": "",
            "rights": "© 2013 New York Academy of Sciences.",
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            "tags": [
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                    "tag": "Visual Cortex",
                    "type": 1
                },
                {
                    "tag": "backprojection",
                    "type": 1
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                {
                    "tag": "feedforward",
                    "type": 1
                },
                {
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                },
                {
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                    "type": 1
                },
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                    "type": 1
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        "meta": {
            "creatorSummary": "Coen et al.",
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            "title": "Dynamic sensory cues shape song structure in Drosophila",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Philip",
                    "lastName": "Coen"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jan",
                    "lastName": "Clemens"
                },
                {
                    "creatorType": "author",
                    "firstName": "Andrew J.",
                    "lastName": "Weinstein"
                },
                {
                    "creatorType": "author",
                    "firstName": "Diego A.",
                    "lastName": "Pacheco"
                },
                {
                    "creatorType": "author",
                    "firstName": "Yi",
                    "lastName": "Deng"
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                    "creatorType": "author",
                    "firstName": "Mala",
                    "lastName": "Murthy"
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            ],
            "abstractNote": "The generation of acoustic communication signals is widespread across the animal kingdom, and males of many species, including Drosophilidae, produce patterned courtship songs to increase their chance of success with a female. For some animals, song structure can vary considerably from one rendition to the next; neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. In direct contrast, here we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male. In particular, using a quantitative behavioural assay combined with computational modelling, we find that males use fast modulations in visual and self-motion signals to pattern their songs, a relationship that we show is evolutionarily conserved. Using neural circuit manipulations, we also identify the pathways involved in song patterning choices and show that females are sensitive to song features. Our data not only demonstrate that Drosophila song production is not a fixed action pattern, but establish Drosophila as a valuable new model for studies of rapid decision-making under both social and naturalistic conditions.",
            "publicationTitle": "Nature",
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            "DOI": "10.1038/nature13131",
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            "language": "en",
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            "rights": "© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.",
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                    "firstName": "Tatyana O",
                    "lastName": "Sharpee"
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                    "firstName": "Craig A",
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                    "firstName": "Christoph E",
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            "abstractNote": "Understanding the neural mechanisms of invariant object recognition remains one of the major unsolved problems in neuroscience. A common solution that is thought to be employed by diverse sensory systems is to create hierarchical representations of increasing complexity and tolerance. However, in the mammalian auditory system many aspects of this hierarchical organization remain undiscovered, including the prominent classes of high-level representations (that would be analogous to face selectivity in the visual system or selectivity to bird's own song in the bird) and the dominant types of invariant transformations. Here we review the recent progress that begins to probe the hierarchy of auditory representations, and the computational approaches that can be helpful in achieving this feat.",
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            "abstractNote": "Areas V1 and V2 of the visual cortex have traditionally been conceived as stages of local feature representations. We investigated whether neural responses carry information about how local features belong to objects. Single-cell activity was recorded in areas V1, V2, and V4 of awake behaving monkeys. Displays were used in which the same local feature (contrast edge or line) could be presented as part of different figures. For example, the same light–dark edge could be the left side of a dark square or the right side of a light square. Each display was also presented with reversed contrast.\nWe found significant modulation of responses as a function of the side of the figure in >50% of neurons of V2 and V4 and in 18% of neurons of the top layers of V1. Thus, besides the local contrast border information, neurons were found to encode the side to which the border belongs (“border ownership coding”). A majority of these neurons coded border ownership and the local polarity of luminance–chromaticity contrast. The others were insensitive to contrast polarity. Another 20% of the neurons of V2 and V4, and 48% of top layer V1, coded local contrast polarity, but not border ownership. The border ownership-related response differences emerged soon (<25 msec) after the response onset. In V2 and V4, the differences were found to be nearly independent of figure size up to the limit set by the size of our display (21°). Displays that differed only far outside the conventional receptive field could produce markedly different responses. When tested with more complex displays in which figure-ground cues were varied, some neurons produced invariant border ownership signals, others failed to signal border ownership for some of the displays, but neurons that reversed signals were rare.\nThe influence of visual stimulation far from the receptive field center indicates mechanisms of global context integration. The short latencies and incomplete cue invariance suggest that the border-ownership effect is generated within the visual cortex rather than projected down from higher levels.",
            "publicationTitle": "The Journal of Neuroscience",
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            "date": "2000",
            "volume": "20",
            "issue": "17",
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            "pages": "6594-6611",
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                    "tag": "Visual Perception",
                    "type": 1
                },
                {
                    "tag": "area V1",
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                },
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                    "tag": "area V2",
                    "type": 1
                },
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                    "tag": "area V4",
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                },
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                    "tag": "awake macaque monkey",
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                },
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                    "tag": "figure-ground segregation",
                    "type": 1
                },
                {
                    "tag": "nonclassical receptive fields",
                    "type": 1
                },
                {
                    "tag": "perceptual organization",
                    "type": 1
                },
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                    "tag": "primate visual cortex",
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            "title": "Border Ownership from Intracortical Interactions in Visual Area V2",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Li",
                    "lastName": "Zhaoping"
                }
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            "abstractNote": "A border between two image regions normally belongs to only one of the regions; determining which one it belongs to is essential for surface perception and figure-ground segmentation. Border ownership is signaled by a class of V2 neurons, even though its value depends on information coming from well outside their classical receptive fields. I use a model of V2 to show that this visual area is able to generate the ownership signal by itself, without requiring any top-down mechanism or external explicit labels for figures, T junctions, or corners. In the model, neurons have spatially local classical receptive fields, are tuned to orientation, and receive information (from V1) about the location and orientation of borders. Border ownership signals that model physiological observations arise through finite range, intraareal interactions. Additional effects from surface features and attention are discussed. The model licenses testable predictions.",
            "publicationTitle": "Neuron",
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            "date": "2005",
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            "issue": "1",
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            "pages": "143-153",
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            "journalAbbreviation": "Neuron",
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