ArticleJournal of vision2019
Using deep learning to probe the neural code for images in primary visual cortex.
Article in Journal of vision, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Hierarchical recurrent temporal prediction as a model of the mammalian dorsal visual pathway.PLoS computational biology · 2026Article
- Deep learning-driven characterization of single cell tuning in primate visual area V4 supports topological organization.bioRxiv : the preprint server for biology · 2025Article
- Article
- Self-Attention-Based Contextual Modulation Improves Neural System Identification.... International Conference on Learning Representations · 2025Article
- Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.Heliyon · 2024Article
- Foundation model of neural activity predicts response to new stimulus types and anatomy.bioRxiv : the preprint server for biology · 2024Article
- Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates.Cell reports · 2024Article
- Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.Nature communications · 2024Article
- The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.ArXiv · 2024Article
- Article
- Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.PLoS computational biology · 2024Article
- Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks.Neural computation · 2024Article
- Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice.Advances in neural information processing systems · 2023Article
- Generalizing biological surround suppression based on center surround similarity via deep neural network models.PLoS computational biology · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.Nature communications · 2023Article
- Statistical image properties and aesthetic judgments on abstract paintings by Robert Pepperell.Journal of vision · 2023Article
- Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice.bioRxiv : the preprint server for biology · 2023Article
- Energy Guided Diffusion for Generating Neurally Exciting Images.bioRxiv : the preprint server for biology · 2023Article
- The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
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Authors and funding
3 authors.
Funding
Abstract
Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in systems neuroscience is devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: These are known as either simple or complex cells. Other neurons respond to localized center-surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells-the best-understood V1 neurons-it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we trained deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and we find that the predicted firing rates are highly correlated (
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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.