Konstantin F. Willeke

This paper presents a high-bandwidth, wireless brain-computer interface device capable of chronic recordings in animals, demonstrating reliable performance over weeks.

The paper identifies a dual-feature encoding strategy in visual cortical neurons where neurons respond to both enhancing and suppressing visual features, showing this strategy is conserved across species.

The paper investigates how macaque V4 neurons respond to natural images using features from CNNs trained on various tasks, finding V4 is best predicted by semantic features and shows task selectivity.

The paper uses deep learning to analyze single-cell neural responses in primate visual area V4, finding functional clustering that mirrors artificial vision systems.

The paper uses CNNs to analyze contextual modulation in visual cortex by synthesizing surround patterns, finding that pattern completion enhances responses while disruption suppresses them, consistent across mice and macaques.

This study shows that foveal visual locations are severely distorted in short-term memory, with the largest errors occurring for the most central targets.

The paper shows that pupil dilation in mice shifts color selectivity in the visual cortex towards ultraviolet stimuli, enhancing detection of ethological features like aerial predators.

This paper introduces a novel readout method for DNNs trained on mouse V1 data, achieving better generalization across animals and outperforming existing models.

This study shows humans and monkeys can voluntarily generate microsaccades on demand without visual guidance or training, with corresponding neural activity in the superior colliculus.