Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization

Scientific Reports
Haiguang WenZhongming Liu

Abstract

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

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Citations

Mar 9, 2019·PLoS Computational Biology·Agustin Lage-CastellanosFederico De Martino
Feb 7, 2020·Journal of Cognitive Neuroscience·Grace W Lindsay
May 16, 2019·PLoS Computational Biology·Karim RajaeiSeyed-Mahdi Khaligh-Razavi
Apr 29, 2020·The European Journal of Neuroscience·Özgür YılmazTolga Çukur
Aug 18, 2020·Computational and Mathematical Methods in Medicine·Ziya YuBin Yan

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Methods Mentioned

BETA
PCA

Software Mentioned

GloVe
ResNet
Google Image search
mathop

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