Summary of Unsupervised Discovery Of the Shared and Private Geometry in Multi-view Data, by Sai Koukuntla et al.
Unsupervised discovery of the shared and private geometry in multi-view data
by Sai Koukuntla, Joshua B. Julian, Jesse C. Kaminsky, Manuel Schottdorf, David W. Tank, Carlos D. Brody, Adam S. Charles
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)
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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 In this paper, researchers develop a novel method to analyze large-scale simultaneous recordings from multiple brain regions. The goal is to understand the relationships between different views of neural activity and uncover fundamental principles about the characteristics of each representation. Existing methods lack expressivity, describe only shared variance, or discard geometric information. The proposed approach uses a nonlinear neural network-based method that disentangles low-dimensional shared and private latent variables while preserving data geometry. The authors demonstrate the effectiveness of their method on simulated and real datasets, including Neuropixels recordings from mice running on a linear track. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how different parts of our brain work together. By recording lots of neural activity at once, scientists can see how different regions are connected. Right now, there aren’t many good ways to study this kind of data. This new method uses a special kind of computer program to help figure out what’s going on in the brain. It works really well and helps us understand things like where an animal is moving based on its brain activity. |
Keywords
» Artificial intelligence » Neural network
