Brain Representation

Brain representation research seeks to understand how the brain encodes information, focusing on aligning artificial neural network models with human brain activity measured via fMRI and EEG. Current efforts utilize various deep learning architectures, including convolutional and transformer networks, often incorporating techniques like contrastive learning and knowledge distillation to improve model-brain alignment. This work is crucial for advancing both neuroscience understanding of brain function and artificial intelligence, potentially leading to improved brain-computer interfaces and more human-like AI systems.