Lynn K. A. Sörensen

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

The paper shows that deep neural networks align more closely with human brain responses to texture-synthesized images than to natural or object-only images, suggesting shared texture statistics rather than object recognition drive this alignment.

Hierarchical Optimization predicts Plasticity in the Macaque Inferior Temporal Cortex following Object Training

The paper models plasticity in macaque inferior temporal cortex after object training using ANN simulations, showing gradient descent approximates observed neural changes.

Human visual cortex and deep convolutional neural network care deeply about object background

This study shows that both human visual cortex and deep convolutional neural networks prioritize object background over category in object recognition tasks.

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

This paper develops deep learning models to study dynamic object recognition, finding that sequential processing with lateral recurrence matches human performance and improves recognition with adaptation.

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model

The paper presents a computational model showing how arousal state modulates task performance by altering sensory processing hierarchy, explaining the Yerkes-Dodson effect.

Leveraging spiking deep neural networks to understand the neural mechanisms underlying selective attention

This study uses spiking deep neural networks to compare attention mechanisms, finding that modulating outgoing connections is more effective than precision or input gain in enhancing visual search performance.