ArticleScience advances2020
Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Perceptual misalignment of texture representations in convolutional neural networks.Journal of vision · 2026Article
- Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales.PLoS computational biology · 2026Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
- Oscillations in an artificial neural network convert competing inputs into a temporal code.PLoS computational biology · 2024Article
- Can we improve techniques and patients' selection for nerve stimulation suitable for lower urinary tract dysfunctions? ICI-RS 2023.Neurourology and urodynamics · 2024Review
- Feature-selective responses in macaque visual cortex follow eye movements during natural vision.Nature neuroscience · 2024Article
- The neural code for "face cells" is not face-specific.Science advances · 2023Article
- Mechanisms of human dynamic object recognition revealed by sequential deep neural networks.PLoS computational biology · 2023Article
- Calibrating Vision: Concepts and Questions.Vision research · 2022Review
- Deep neural models for color classification and color constancy.Journal of vision · 2022Article
- Efficient processing of natural scenes in visual cortex.Frontiers in cellular neuroscience · 2022Review
- Temporal stability of stimulus representation increases along rodent visual cortical hierarchies.Nature communications · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.
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Registered trials
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.