ArticleProceedings of the National Academy of Sciences of the United States of America2019
Brain-inspired automated visual object discovery and detection.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- A Biomimetic Visual Sensing Framework: Unsupervised Orientation Topographic Mapping via Self-Organizing Neural Networks.Biomimetics (Basel, Switzerland) · 2026Article
- Reading specific memories from human neurons before and after sleep.bioRxiv : the preprint server for biology · 2025Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- A Gaussian Mixture Model-Based Unsupervised Dendritic Artificial Visual System for Motion Direction Detection.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (
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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.