Summary of Finding Shared Decodable Concepts and Their Negations in the Brain, by Cory Efird et al.
Finding Shared Decodable Concepts and their Negations in the Brain
by Cory Efird, Alex Murphy, Joel Zylberberg, Alona Fyshe
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A multimodal neural network architecture, called CLIP, is used to train a highly accurate contrastive model that maps brain responses during naturalistic image viewing to CLIP embeddings. The model is trained on a combination of natural language and image data. A novel adaptation of the DBSCAN clustering algorithm is then applied to cluster the parameters of these participant-specific contrastive models, revealing Shared Decodable Concepts (SDCs) that are decodable from common sets of voxels across multiple participants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have found that different parts of our brain are good at processing certain types of visual information. For example, one area is great at recognizing faces. However, there’s a lot more to see in the world than just faces! Using a special kind of computer program called CLIP, researchers trained a model that could understand what people were looking at when they viewed images. By using this model and a special clustering technique, the researchers found patterns or “Shared Decodable Concepts” that were common across many people’s brains. |
Keywords
» Artificial intelligence » Clustering » Neural network
