ArticleProceedings of the National Academy of Sciences of the United States of America2025
Image segmentation with traveling waves in an exactly solvable recurrent neural network.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Spatially Structured Spiking Network Model of Beta Traveling Waves and Their Attenuation in Motor Cortex.bioRxiv : the preprint server for biology · 2026Article
- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- A proposal for realizing cognitive functions using traveling waves, phase-locked and synchronized patterns, holography, neural mixing, and single sideband communications.Frontiers in computational neuroscience · 2026Article
- Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics.ArXiv · 2025Article
- The functional role of oscillatory dynamics in neocortical circuits: A computational perspective.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Transformers and cortical waves: encoders for pulling in context across time.Trends in neurosciences · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We study image segmentation using spatiotemporal dynamics in a recurrent neural network where the state of each unit is given by a complex number. We show that this network generates sophisticated spatiotemporal dynamics that can effectively divide an image into groups according to a scene's structural characteristics. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images. Using an exact solution of the recurrent network's dynamics, we present a precise description of the mechanism underlying object segmentation in the network dynamics, providing a clear mathematical interpretation of how the algorithm performs this task. Object segmentation across all images is accomplished with one recurrent neural network that has a single, fixed set of weights. This demonstrates the expressive potential of recurrent neural networks when constructed using a mathematical approach that brings together their structure, dynamics, and computation.
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Registered trials
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