ArticlePLoS computational biology2021
Learning divisive normalization in primary visual cortex.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 49 citations in OpenAlex.
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- Dual-feature selectivity enables bidirectional coding in visual cortical neurons.bioRxiv : the preprint server for biology · 2025Article
- Learning to cluster neuronal function.ArXiv · 2025Article
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- FOUNDATIONS OF VISUAL FORM SELECTIVITY IN MACAQUE AREAS V1 AND V2.bioRxiv : the preprint server for biology · 2025Article
- A Turing Test for artificial nets devoted to vision.Frontiers in artificial intelligence · 2025Article
- Feature-specific divisive normalization improves natural image encoding for depth perception.bioRxiv : the preprint server for biology · 2024Article
- Foundation model of neural activity predicts response to new stimulus types and anatomy.bioRxiv : the preprint server for biology · 2024Article
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- The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.ArXiv · 2024Article
- Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks.PLoS computational biology · 2024Article
- Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.Journal of vision · 2024Article
- Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Response sub-additivity and variability quenching in visual cortex.Nature reviews. Neuroscience · 2024Review
- Article
- Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd.Frontiers in neuroscience · 2024Article
- Normalization by orientation-tuned surround in human V1-V3.PLoS computational biology · 2023Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed.
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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.