Barry J. Devereux

The paper uses a convolutional neural network to model MEG responses during visual word recognition, showing it can predict key evoked response components and simulate reading processes.

This study compares speech recognition representations in artificial neural networks and human brains, finding similarities and using brain data to improve machine models.

The paper uses fMRI and representational similarity analysis to show that both perceptual and semantic representations contribute to true and false recognition of objects, with fine-grained features aiding accurate recall and coarse categories linked to forgetting.

This study proposes a zero-shot EEG-to-image decoding approach for image retrieval, achieving competitive accuracy across two EEG datasets using biologically inspired models.

The paper investigates how visual information transforms into semantic representations in the ventral visual pathway using computational models and MEG data, finding distinct oscillatory patterns for visual and semantic information.

The paper combines deep visual and semantic attractor networks to model fMRI data from object processing, showing how visual input influences semantic representations in the ventral stream.