Summary of Data Augmentation For Automated Adaptive Rodent Training, by Dibyendu Das et al.
Data Augmentation for Automated Adaptive Rodent Training
by Dibyendu Das, Alfredo Fontanini, Joshua F. Kogan, Haibin Ling, C.R. Ramakrishnan, I.V. Ramakrishnan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper presents a data-driven approach to optimize lab animal training protocols, specifically for rodents. By leveraging data augmentation techniques, the researchers built artificial rodent models and developed a novel similarity metric based on action probability distributions to measure behavioral resemblance with real rodents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative study aims to revolutionize the way researchers train lab animals like rodents, a labor-intensive process that requires close interaction between the animal and researcher. The goal is to develop an efficient and automatic trainer using artificial rodent models built through data augmentation techniques. |
Keywords
» Artificial intelligence » Data augmentation » Probability
