Sander Bosch

This paper reconstructs faces from fMRI data using GAN latent spaces, achieving accurate perception reconstructions by aligning neural responses with generator features.

This study uses a neural network model to show how neurons represent and incorporate prior knowledge through environmental regularities, without probabilistic feedback.

DeepRF uses deep learning to rapidly estimate population receptive fields, outperforming traditional methods in speed and accuracy.

The paper investigates how mental imagery influences conscious perception by analyzing individual differences in the interaction between imagery and sensory input using a Bayesian hierarchical model.

This study examines the temporal dynamics of visual imagery versus perception, finding distinct patterns in the timing of neural activity.

The paper uses CNNs to model visual object recognition in MEG data, showing a feedforward sweep across the visual hierarchy and achieving high decoding accuracy.

The paper proposes using reinforcement learning neural networks to model cognitive processes by linking network dynamics to human behavior and neural data in a probabilistic categorization task.