ArticleNature neuroscience2019
Inception loops discover what excites neurons most using deep predictive models.
Article in Nature neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.
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Who cites it
113 citing papers in PubMed.
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- The functional organization of retinal input to the mouse superior colliculus.bioRxiv : the preprint server for biology · 2026Article
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- Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex.Nature neuroscience · 2026Article
- Functional bipartite invariance in mouse primary visual cortex receptive fields.Nature neuroscience · 2026Article
- Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks.Nature communications · 2026Article
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- Individual differences in artificial neural networks capture individual differences in human behavior.bioRxiv : the preprint server for biology · 2026Article
- Mapping the visual cortex with Zebra noise and wavelets.Journal of vision · 2026Article
- Higher-level spatial prediction in natural vision across mouse visual cortex.PLoS computational biology · 2026Article
- A TALE OF TWO TAILS: PREFERRED AND ANTI-PREFERRED NATURAL STIMULI IN VISUAL CORTEX.... International Conference on Learning Representations · 2026Article
- Dual-feature selectivity enables bidirectional coding in visual cortical neurons.bioRxiv : the preprint server for biology · 2025Article
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- A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels.Nature electronics · 2025Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
53 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
Abstract
Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.
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31686023What OpenQuestion holds
Registered trials
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