Summary of Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses, by Yasaman Parhizkar et al.
Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses
by Yasaman Parhizkar, Gene Cheung, Andrew W. Eckford
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV); Neurons and Cognition (q-bio.NC); Quantitative Methods (q-bio.QM)
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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 deep neural network is trained on data to predict the firings of ganglion cells in response to visual stimuli. While current approaches can accurately predict these firings, they lack interpretability and do not provide insights into the underlying operations of the cells. To address this, the authors propose an interpretable graph-based classifier that learns a positive semi-definite metric matrix to define Mahalanobis distances between visual events. The learned metric matrix provides interpretability by identifying important features along its diagonal and their mutual relationships through off-diagonal terms. This approach can be applied to other biological systems with pre-chosen features, enabling the interpretation of complex data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to understand how our brain cells work when we see something. Currently, computers can guess what these cells are doing, but it’s hard to know why they’re making those guesses. The authors want to change this by creating a special kind of computer program that can learn from data and explain itself in simple terms. They do this by using a combination of math and computer science techniques to create a graph (a visual map) of the brain cells’ behavior. This allows them to see which features are important and how they relate to each other, giving us new insights into how our brains work. |
Keywords
* Artificial intelligence * Neural network
