August 26, 2025Open Access

Comprehensive Neural Representations of Naturalistic Stimuli through Multimodal Deep Learning

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Deep learning models predict whole-brain responses more accurately, emphasizing multimodal integration and cognition.
Results show improved predictions compared to traditional unimodal and static models, enhancing ecological validity.
A video-text alignment encoding framework enables the mapping of cortical semantic spaces without manual labeling.
Findings reveal a hierarchical predictive coding gradient in the brain, linking cognitive abilities to temporal event anticipation.

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Innovative neural encoding framework predicts brain activity using multimodal integration, highlighting naturalistic cognition.

Fu et al. (2025) studied this question.

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