ArticleNature communications2022
Efficient neural codes naturally emerge through gradient descent learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative Effectiveness of Different Targeted Temperature Management Modalities for Adult Patients with Traumatic Brain Injury.Neurocritical care · 2026Pooled it
- Intracellular pathogen manipulation of ER-phagy: direct receptor targeting and indirect pathway perturbation.Medical microbiology and immunology · 2026Review
- Synthetic microbial communities: from theoretical exploration to applied innovation.World journal of microbiology & biotechnology · 2026Review
- A reduced perception of sensory information is linked with elevated boredom in people with and without attention-deficit hyperactivity disorder.Communications psychology · 2025Article
- Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment.bioRxiv : the preprint server for biology · 2025Article
- Adaptation optimizes sensory encoding for future stimuli.PLoS computational biology · 2025Article
- Isotschimgine promotes lifespan, healthspan and neuroprotection of Caenorhabditis elegans via the activation of nuclear hormone receptors.Biogerontology · 2024Article
- Jointly efficient encoding and decoding in neural populations.PLoS computational biology · 2024Article
- Precise genome-editing in human diseases: mechanisms, strategies and applications.Signal transduction and targeted therapy · 2024Review
- Asymmetric stimulus representations bias visual perceptual learning.Journal of vision · 2024Article
- Boredom and curiosity: the hunger and the appetite for information.Frontiers in psychology · 2024Review
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Curriculum Learning with Infant Egocentric Videos.Advances in neural information processing systems · 2023Article
- Touch, press and stroke: a soft capacitive sensor skin.Scientific reports · 2023Article
- Generalizing biological surround suppression based on center surround similarity via deep neural network models.PLoS computational biology · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural networks trained to recognize objects also have patterns of sensitivity that match the statistics of features in images. To interpret these findings, we show mathematically that learning with gradient descent in neural networks preferentially creates representations that are more sensitive to common features, a hallmark of efficient coding. This effect occurs in systems with otherwise unconstrained coding resources, and additionally when learning towards both supervised and unsupervised objectives. This result demonstrates that efficient codes can naturally emerge from gradient-like learning.
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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.