Scaling Up Neural Datasets: A public fMRI dataset of 5000 scenes

Chang, Nai Chen; Aminoff, Elissa; Pyles, John A.; Tarr, Michael J.; Gupta, Abhinav

doi:10.1167/18.10.732

Cited by 1 publication

(1 citation statement)

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“…Compared with the high-dimensional features instantiated in AlexNet, we did not have a sufficient number of observations to fit large regression models that included all dimensions in the features, or to use higher-capacity and non-linear models. In future work, it is important to vastly increase the number of observations (as in (Chang et al, 2018)), by increasing data collection time or, perhaps, by reducing the number of image repetitions but increasing measurement SNR.…”

Section: Confounding Factors In Data-driven Experimentsmentioning

confidence: 99%

Exploring spatio-temporal neural dynamics of the human visual cortex

Yang

Tarr

Aminoff

2018

Preprint

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The human visual cortex is organized in a hierarchical manner. Although a significant body of evidence has been accumulated in support of this hypothesis, specific details regarding the spatial and temporal information flow remain open. Here we present detailed spatio-temporal correlation profiles of neural activity with low-level and high-level features derived from a "deep" (8-layer) neural network pre-trained for object recognition. These correlation profiles indicate an early-to-late shift from lowlevel features to high-level features and from low-level regions to higher-level regions along the visual hierarchy, consistent with feedforward information flow. To refine our understanding of information flow, we computed three sets of features from the low-and high-level features provided by the neural network: object-category-relevant low-level features (the common components between low-level and high-level features), low-level features roughly orthogonal to high-level features (the residual Layer 1 features), and unique high-level features that were roughly orthogonal to low-level features (the residual Layer 7 features). Contrasting the correlation effects of the common components and the residual Layer 1 features, we observed that the early visual cortex exhibits a similar amount of correlation with the two feature sets early in time (60 to 120 ms), but in a later time window, the early visual cortex exhibits a higher and longer correlation effect with the common components/low-level task-relevant features as compared to the low-level residual features-an effect unlikely to arise from purely feedforward information flow. Overall, our results indicate that non-feedforward processes, for example, top-down influences from mental representations of categories, may facilitate differentiation between these two types of low-level features within the early visual cortex. Common components between the low-level and high-level features, or the subspace of low-level featuresthat are relevant to high-level features.2. Residuals of low-level features. These are the low-level features that are roughly orthogonal to high-level features, or the features remaining after regressing out the common components.3. Residuals of high-level features. These are the high-level features that are roughly orthogonal to lowlevel features, or the features remaining after regressing out the common components.

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Section: Confounding Factors In Data-driven Experimentsmentioning

confidence: 99%