Michael Bonner

The paper examines the temporal dynamics and latent dimensional structure of natural object representations in EEG and MEG data, revealing rapid dimensionality expansion and unexplained neural variance.

The paper shows that convolutional neural networks with minimal training can produce cortex-aligned visual representations through specific dimensionality manipulations, suggesting architectural constraints are key.

This study shows that high-dimensional latent spaces improve generalization in DNN models of visual cortex when predicting cortical responses to new stimuli.

The paper shows feedforward models of the PPA exhibit both high-level scene/object selectivity and low-level contour preferences, reconciling disparate theories of PPA function.

This study maps brain regions to deep neural network tasks using fMRI data, showing structured correlations between tasks and visual cortex streams.

The paper uses fMRI and machine learning to show that object representations in the human brain reflect co-occurrence statistics of objects in vision and language.

The paper uses deep convolutional neural networks to model cortical responses to navigational affordances in visual scenes, finding that feedforward computations capture key features of scene layout.

The paper investigates how perirhinal cortex encodes semantic knowledge of objects by dissociating perceptual features from semantic meaning using fMRI and multivoxel-pattern analysis.