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Published on: May 2, 2019
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Population encoding of stimulus features along the visual hierarchy.
Luciano Dyballa1, Andra M Rudzite2, Mahmood S Hoseini3
1Department of Computer Science, Yale University.
Biorxiv : the Preprint Server for Biology
|July 10, 2023
Summary
Neural populations in the retina discretely encode visual features, while the primary visual cortex (V1) shows a continuous representation. This suggests artificial neural networks resemble retinas more than brains.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Vision Science
Background:
- The retina and primary visual cortex (V1) contain diverse neural populations sensitive to various visual features.
- Understanding how these neural populations partition stimulus space to represent features is crucial but remains unclear.
Conclusions:
- Biological visual processing exhibits distinct feature partitioning strategies between the retina and V1.
- CNNs used in visual processing models appear to more closely mimic retinal processing than cortical processing.
- This suggests current artificial neural networks may be more analogous to 'big retinas' than 'little brains'.
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