ArticleCerebral cortex (New York, N.Y. : 1991)2018
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.
Article in Cerebral cortex (New York, N.Y. : 1991), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 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
98 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Talking to the Brain: Using Large Language Models as Proxies to Model Brain Semantic Features.Human brain mapping · 2026Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- Theoretical Framework, Technical Evolution, and Future Prospects of Cross-Modal Mapping and Controllable Image Generation Under Multi-Source Heterogeneous Collaboration.Sensors (Basel, Switzerland) · 2026Review
- Movie Features Are Hierarchically Encoded in the Dynamics of Functional Connectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Bidirectional cross-day alignment of neural spikes and behavior using a hybrid SNN-ANN algorithm.Frontiers in neuroscience · 2026Article
- Structured interconnectivity optimizes neural geometry for balancing specificity and generalization in object recognition.Communications biology · 2025Article
- Brain-AI Alignment in Naturalistic Movies.bioRxiv : the preprint server for biology · 2025Article
- Neural Encoding and Decoding at Scale.ArXiv · 2025Article
- Deep learning health space model for ordered responses.BMC medical informatics and decision making · 2025Article
- Object representations drive emotion schemas across a large and diverse set of daily-life scenes.Communications biology · 2025Article
- Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.Micromachines · 2025Article
- Compression-enabled interpretability of voxelwise encoding models.PLoS computational biology · 2025Article
- Ultra high density imaging arrays in diffuse optical tomography for human brain mapping improve image quality and decoding performance.Scientific reports · 2025Article
- An action decoding framework combined with deep neural network for predicting the semantics of human actions in videos from evoked brain activities.Frontiers in neuroinformatics · 2025Article
- Multisensory naturalistic decoding with high-density diffuse optical tomography.Neurophotonics · 2025Article
- The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- An fMRI dataset in response to large-scale short natural dynamic facial expression videos.Scientific data · 2024Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Gustatory interface for operative assessment and taste decoding in patients with tongue cancer.Nature communications · 2024Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.
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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.