Connectome simulations identify a central pattern generator for fly walking

Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input.

By Pugliese, Chou, Abe +4NatureOct 28, 2026

Score████░░░░░░4.4

VerdictWorth a reader's time today.

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Abstract

Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input. However, the circuit structure of a CPG for walking is not known in any animal1,2. To identify the cells and synapses that underlie rhythmic leg movement in walking flies, we developed dynamic simulations of the Drosophila ventral nerve cord connectomes3-6. A computational activation screen of descending neurons from the central brain identified DNg100-a known command neuron for walking-as the strongest driver of rhythmic leg motor activity. Simulated network pruning isolated a minimal rhythm-generating circuit consisting of one inhibitory and two excitatory interneurons; this three-neuron circuit was necessary and sufficient for motor rhythms across all six legs and in four connectome datasets. Simulations also predicted that a separate descending pathway (DNb08) drives rhythmic leg movements, which we confirmed experimentally using optogenetics in behaving flies. Our results reveal the cellular identity and synaptic structure of a putative CPG circuit for walking and other rhythmic leg movements in flies.

Sarah M Pugliese, Grant M Chou, Elliott T T Abe, Denis Turcu, Jackson K Lancaster, John C Tuthill, Bingni W Brunton

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 16% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 20% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 10% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░3/5	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░3/5	16%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░3/5	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	███░░3/5	20%	A genuinely new approach to an open problem.
Trajectory	██░░░2/5	10%	Some room to improve with obvious engineering.
Stakes	██░░░2/5	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (many tasks); verification (multiple benchmarks, independent replication).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.4

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.6 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.6 / 10; Shrunk toward the desk prior by editor confidence (36%).
Attention: 31%; Citations, upvotes, points, mentions.
Freshness: 49%; Half-life decay since publication.

Citations10 (reference 20, via openalex, Nov 4, 2026, 05:48 UTC)

The record

Reviewed by heuristic-v10 on Oct 30, 2026, 05:48 UTC. Paper type: empirical.
Categories: Journal Article
BRIEF, No.8 in the Biology edition of November 4, 2026.