Lars F. Muckli

The paper investigates memory-based and surround-based pattern completion in human V1 using fMRI, finding distinct laminar decoding profiles for each mechanism.

This study uses fMRI to compare theories about how action affects sensory processing in early visual cortex, finding support for opposing processes theory but also unexpected results.

The paper investigates how feedback signals related to objects and scenes are organized in V1, finding that object feedback is foveal and scene feedback is both foveal and peripheral, with task demands affecting scene feedback.

The paper shows how visual cortex neurons adjust their responses to direct inputs and contextual signals through opposing plasticity mechanisms, enhancing improbable inputs or omissions.

The paper investigates how real and imaginary experiences are processed in V1 using fMRI and pattern analysis, finding distinct decoding depths for illusions and imagery.

The paper shows that perceptual priors enhance feedback processing in early visual areas when re-exposed to previously ambiguous images.

The paper shows feedback in human early visual cortex contains both concurrent contextual and time-distant mnemonic information.

The paper shows that V1 neurons in monkeys and humans respond to occluded scenes using contextual information, with representations resembling those in humans.

CEREBRUM-7T is a fast, fully-volumetric brain segmentation method for 7T MRI that outperforms its training labels and is suitable for neuroimaging studies.

This paper compares a self-supervised CNN for image completion with fMRI data from early visual cortex, finding the CNN's decoder pathway more similar to brain activity than a supervised model.

The paper uses fMRI and multi-voxel pattern analysis to show that increased contextual surround visibility enhances feedback information and feedforward processing in human V1.

This study demonstrates line-scanning fMRI as a high-resolution non-invasive method to map functional properties of human cortical layers during visual stimulation.

The paper evaluates the spatial precision of stimulus-specific BOLD responses using MVPA across cortical depths in V1, finding that multivoxel patterns can achieve sub-millimeter resolution.

This paper transfers deep neural network features to fMRI data to improve decoding performance by reconstructing CNN features from imaging data.

The paper uses fMRI data to show that internal visual models in early cortex are categorical and scene-specific, and that line drawings effectively describe their structure.

This paper introduces tMVPA, a method to analyze temporal dynamics of the BOLD signal in fMRI data using single-trial analysis and sliding windows, showing it can detect time-specific patterns with controlled error rates.

The paper investigates how predictive feedback in V1 updates with eye movements using an apparent motion illusion and functional brain imaging.