Amber Marijn Brands

This study evaluates how different temporal adaptation mechanisms in deep convolutional neural networks affect object recognition under various challenging viewing conditions.

The paper investigates why deep convolutional neural networks (DCNNs) with higher object recognition accuracy perform worse at predicting EEG responses, finding that EEG signals reflect scene segmentation rather than object-specific representations.

This paper evaluates whether PredNet, a deep learning model based on predictive coding, captures neural signatures of short-term visual adaptation in human visual cortex, finding partial alignment with neural data.

This study shows that temporal adaptation improves sensory representations and identifies neural computations, like divisive normalization, that mediate these benefits in human visual object recognition.

The paper examines how neural adaptation in human visual cortex varies across the visual hierarchy using iEEG data and a computational model of normalization dynamics.