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- W2148686323 abstract "Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus." @default.
- W2148686323 created "2016-06-24" @default.
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- W2148686323 date "2014-08-01" @default.
- W2148686323 modified "2023-09-27" @default.
- W2148686323 title "ROC-Based Estimates of Neural-Behavioral Covariations Using Matched Filters" @default.
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- W2148686323 doi "https://doi.org/10.1162/neco_a_00616" @default.
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