ArticlePatterns (New York, N.Y.)2021
Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection.Frontiers in computational neuroscience · 2025Article
- Decoding dynamic visual scenes across the brain hierarchy.PLoS computational biology · 2024Article
- The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.ArXiv · 2024Article
- Article
- Exploiting noise as a resource for computation and learning in spiking neural networks.Patterns (New York, N.Y.) · 2023Article
- Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Representing the dynamics of high-dimensional data with non-redundant wavelets.Patterns (New York, N.Y.) · 2022Article
- Retinal Processing: Insights from Mathematical Modelling.Journal of imaging · 2022Article
- Dissecting cascade computational components in spiking neural networks.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional models of retinal system identification analyze the neural response to artificial stimuli using models consisting of predefined components. The model design is limited to prior knowledge, and the artificial stimuli are too simple to be compared with stimuli processed by the retina. To fill in this gap with an explainable model that reveals how a population of neurons work together to encode the larger field of natural scenes, here we used a deep-learning model for identifying the computational elements of the retinal circuit that contribute to learning the dynamics of natural scenes. Experimental results verify that the recurrent connection plays a key role in encoding complex dynamic visual scenes while learning biological computational underpinnings of the retinal circuit. In addition, the proposed models reveal both the shapes and the locations of the spatiotemporal receptive fields of ganglion cells.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.