ArticleScientific reports2019
Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Cortical state contributions to neuronal response variability in the early visual cortex: A system identification approach.PLoS computational biology · 2025Article
- A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT.EJNMMI research · 2025Article
- Feature selectivity and invariance in marsupial primary visual cortex.The Journal of physiology · 2025Article
- Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes.Nature communications · 2024Article
- Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.PLoS computational biology · 2024Article
- Applications of artificial intelligence in dementia.Geriatrics & gerontology international · 2024Review
- Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.Scientific reports · 2024Article
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- Motor-related signals support localization invariance for stable visual perception.PLoS computational biology · 2022Article
- Figure-ground responsive fields of monkey V4 neurons estimated from natural image patches.PloS one · 2022Article
- GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability.Frontiers in neuroscience · 2021Article
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3 authors.
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Abstract
A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.
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