ArticleScientific reports2020
Evidence for the intrinsically nonlinear nature of receptive fields in vision.
Article in Scientific reports, 2020. 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, 24 citations in OpenAlex.
- A Neural Model for V1 That Incorporates Dendritic Nonlinearities and Backpropagating Action Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Wave Propagation Phenomena in Nonlinear Hierarchical Neural Networks with Predictive Coding Feedback Dynamics.Bulletin of mathematical biology · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- Overcoming the limitations of motion sensor models by considering dendritic computations.Scientific reports · 2025Article
- Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks.Entropy (Basel, Switzerland) · 2025Article
- A Turing Test for artificial nets devoted to vision.Frontiers in artificial intelligence · 2025Article
- Plaid masking explained with input-dependent dendritic nonlinearities.Scientific reports · 2024Article
- Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- State-of-the-art image and video quality assessment with a metric based on an intrinsically non-linear neural summation model.Frontiers in neuroscience · 2023Article
- On the synthesis of visual illusions using deep generative models.Journal of vision · 2022Article
- Contrast sensitivity functions in autoencoders.Journal of vision · 2022Article
- Neurobiological reduction: From cellular explanations of behavior to interventions.Frontiers in psychology · 2022Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). The linear RF has a number of inherent problems: it changes with the input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations. Here we propose to model the RF in a nonlinear manner, introducing the intrinsically nonlinear receptive field (INRF). Apart from being more physiologically plausible and embodying the efficient representation principle, the INRF has a key property of wide-ranging implications: for several vision science phenomena where a linear RF must vary with the input in order to predict responses, the INRF can remain constant under different stimuli. We also prove that Artificial Neural Networks with INRF modules instead of linear filters have a remarkably improved performance and better emulate basic human perception. Our results suggest a change of paradigm for vision science as well as for artificial intelligence.
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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.