Summary of Revealing Vision-language Integration in the Brain with Multimodal Networks, by Vighnesh Subramaniam et al.
Revealing Vision-Language Integration in the Brain with Multimodal Networks
by Vighnesh Subramaniam, Colin Conwell, Christopher Wang, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to investigating multimodal integration in the human brain using deep neural networks (DNNs). The authors use DNNs to predict stereoencephalography (SEEG) recordings taken while subjects watch movies, operationalizing sites of multimodal integration as regions where the DNN model predicts recordings better than unimodal language, vision, or linearly-integrated language-vision models. The paper explores different architectures and training techniques for the DNN models, including convolutional networks and transformers, cross-attention, and contrastive learning. The authors first demonstrate that trained vision and language models outperform their randomly initialized counterparts in predicting SEEG signals. They then compare unimodal and multimodal models against each other, finding a sizable number of neural sites (12.94%) where multimodal integration occurs. Among the variants of multimodal training techniques assessed, CLIP-style training is found to be the best suited for downstream prediction of neural activity in these sites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computers called deep neural networks to figure out how our brains work when we see and hear things at the same time. The researchers recorded brain activity while people watched movies and then used the computer models to predict what was happening in their brains. They found that some parts of the brain are really good at combining visual and auditory information, and they identified which parts those were. They also discovered that one type of training for these computers works better than others when trying to understand brain activity. |
Keywords
» Artificial intelligence » Cross attention
