ArticlePLoS computational biology2016
Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Learning to cluster neuronal function.ArXiv · 2025Article
- Broadband visual stimuli improve neuronal representation and sensory perception.Nature communications · 2025Article
- Foundation model of neural activity predicts response to new stimulus types and anatomy.bioRxiv : the preprint server for biology · 2024Article
- The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.ArXiv · 2024Article
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- Probabilistic neural transfer function estimation with Bayesian system identification.PLoS computational biology · 2024Article
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- Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice.Advances in neural information processing systems · 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
- Energy Guided Diffusion for Generating Neurally Exciting Images.bioRxiv : the preprint server for biology · 2023Article
- Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes.Entropy (Basel, Switzerland) · 2023Article
- AnFrontiers in neuroscience · 2023Article
- High-Level Visual Encoding Model Framework with Hierarchical Ventral Stream-Optimized Neural Networks.Brain sciences · 2022Article
- Contrast sensitivity functions in autoencoders.Journal of vision · 2022Article
- Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks.Patterns (New York, N.Y.) · 2021Article
- Learning divisive normalization in primary visual cortex.PLoS computational biology · 2021Article
- End-to-end neural system identification with neural information flow.PLoS computational biology · 2021Article
- Inception loops discover what excites neurons most using deep predictive models.Nature neuroscience · 2019Article
- Deep convolutional models improve predictions of macaque V1 responses to natural images.PLoS computational biology · 2019Article
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4 authors.
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Abstract
Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.
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