Neural Comput - ROC-based estimates of neural-behavioral covariations using matched filters.

Tópicos

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Resumo

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.

Resumo Limpo

correl respons visual cortex perceptu perform help draw function link neural activ visual guid behavior correl common deriv rocbas neuralbehavior covari refer choic detect probabl use boxcar analysi window although boxcar window captur covari neural activ behavior steadyst stimulus present optim captur correl short timevari visual input studi implement matchedfilt techniqu combin crossvalid improv estim rocbas neuralbehavior covari short dynam stimulus condit show approach maxim area roc curv converg true neuralbehavior covari use poisson spike model also demonstr match filter combin crossvalid reveal dynam neuralbehavior covari individu mt neuron detect brief motion stimulus

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