Salmela, V. R. | bioRxiv Science

bioRxiv2023

Entropy predicts early MEG, EEG and fMRI responses to natural images

To reduce the redundancy in the input, the human visual system employs efficient coding. Therefore, images with varying entropy (amount of information) should elicit distinct brain responses. Here, we show that a simple entropy model outperforms all current models, including many deep neural networks, in predicting early MEG/EEG and fMRI responses to visual objects. This suggests that the neural populations in the early visual cortex adapt to the information in natural images.

neuroscience↗

bioRxiv2021

Prolonged and distributed processing of facial identity in the human brain

The recognition of facial identity is essential for social interactions. Despite extensive prior fMRI and EEG/MEG research on the neural representations of familiar faces, we know little about the spatio-temporal dynamics of face identity information. Therefore, we applied a novel multimodal approach by fusing the neuronal responses recorded in an fMRI and an EEG experiment. We analyzed the neural responses to naturally varying famous faces and traced how face identity emerges over time in different areas of the brain. We found that image invariant face identity information prevails over an extended time period (from 150 to 810 ms after stimulus onset) in the representational geometry of a broadly distributed network of parietal, temporal, and frontal areas with overlapping temporal profiles. These results challenge the current hierarchical models of face perception and suggest instead concerted and parallel activation of multiple nodes in the brains identity coding network while processing information of familiar faces.

neuroscience↗