ArticleAdvances in neural information processing systems2019
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction.
Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Temporal prediction captures retinal spiking responses across animal species.Nature communications · 2026Article
- Article
- Machine Learning Techniques for Simulating Human Psychophysical Testing of Low-Resolution Phosphene Face Images in Artificial Vision.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Adaptation of retinal discriminability to natural scenes.bioRxiv : the preprint server for biology · 2024Article
- Article
- Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses.Nature communications · 2024Article
- Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.Nature communications · 2024Article
- Encoding surprise by retinal ganglion cells.PLoS computational biology · 2024Article
- Information Geometry of the Retinal Representation Manifold.bioRxiv : the preprint server for biology · 2023Article
- Article
- Scalable Gaussian process inference of neural responses to natural images.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Learning low-dimensional generalizable natural features from retina using a U-net.Advances in neural information processing systems · 2022Article
- Neural representational geometry underlies few-shot concept learning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration.PLoS computational biology · 2022Article
- Retinal Processing: Insights from Mathematical Modelling.Journal of imaging · 2022Article
- Tinnitus is associated with improved cognitive performance and speech perception-Can stochastic resonance explain?Frontiers in aging neuroscience · 2022Article
- Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks.Patterns (New York, N.Y.) · 2021Article
- Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (a biological circuit) with another (a deep network), without understanding either? Moreover, beyond neural representations, are the deep network's
Identifiers
35283616PMC8916592What OpenQuestion holds
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.