ArticlePLoS computational biology2019
Deep convolutional models improve predictions of macaque V1 responses to natural images.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers.
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Who cites it
146 citing papers in PubMed.
- Temporal prediction captures retinal spiking responses across animal species.Nature communications · 2026Article
- Perceptual misalignment of texture representations in convolutional neural networks.Journal of vision · 2026Article
- Top-down feedback in deep neural networks leads to functional differences during audiovisual integration.eLife · 2026Article
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- Monkey upload: Improving robustness using multi-stage neural alignment.Journal of vision · 2026Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli.eNeuro · 2026Article
- Article
- Functional bipartite invariance in mouse primary visual cortex receptive fields.Nature neuroscience · 2026Article
- Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies.PLoS computational biology · 2026Article
- Active gaze behavior organizes V1 activity in freely-moving marmosets.bioRxiv : the preprint server for biology · 2026Article
- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- Recurrent connections facilitate occluded object recognition by explaining-away.Nature communications · 2026Article
- Mapping the visual cortex with Zebra noise and wavelets.Journal of vision · 2026Article
- A TALE OF TWO TAILS: PREFERRED AND ANTI-PREFERRED NATURAL STIMULI IN VISUAL CORTEX.... International Conference on Learning Representations · 2026Article
- Approaching human visual perception through AI-based representation of figure-ground segregation.Frontiers in psychology · 2026Article
- Synthesizing images to map neural networks to the human brain.bioRxiv : the preprint server for biology · 2025Article
- 101 Dalmatians: a multimodal naturalistic fMRI dataset in typical development and congenital sensory loss.Scientific data · 2025Article
- Impact of a transient neonatal visual deprivation on the development of the ventral occipito-temporal cortex in humans.Nature communications · 2025Article
- Digital dementia and testing of cognitive intervention for degenerating neural networks.NPJ systems biology and applications · 2025Article
86 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.
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