ArticleSIAM journal on applied dynamical systems2022
Sequential Attractors in Combinatorial Threshold-Linear Networks.
Article in SIAM journal on applied dynamical systems, 2022. 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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6 citing papers in PubMed.
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.Nature neuroscience · 2024Article
- Stable fixed points of combinatorial threshold-linear networks.Advances in applied mathematics · 2024Article
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- Free recall scaling laws and short-term memory effects in a latching attractor network.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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4 authors.
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Abstract
Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of
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