Machine Learning Enhances Retinal Prostheses, Mimics Natural Processing
Researchers develop a machine learning framework that improves image downsampling for retinal implants, closely replicating the retina's natural response.
- A collaborative effort by EPFL researchers has led to the creation of a machine learning framework that optimizes image downsampling for retinal prostheses.
- The actor-model framework, utilizing two neural networks, mimics aspects of retinal processing and finds the optimal balance for image contrast.
- Validation tests on digital retina models and explanted mouse retinas show that the machine learning approach elicits responses more similar to natural retinal processing than traditional methods.
- The study, published in Nature Communications, marks a significant advancement in neuroprosthetics and sensory encoding.
- Potential future applications of the framework extend beyond vision restoration, including auditory and limb prostheses.
ScienceDaily 
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