Firefly swarm

Fireflies twinkle against a backdrop of stars in Congaree National Park. (Credit: Nolan Bonnie)

In A Nutshell

  • Researchers spent five years at Congaree National Park measuring how individual Photuris frontalis fireflies adjust their flash timing when exposed to an artificial blinking light, producing the first experimentally measured timing rulebook for any North American firefly species.
  • Each firefly uses a two-directional response: it speeds up its next flash when a light appears just before it was about to blink, and slows down when a light appears just after it blinked, a push-pull dynamic that mathematicians had long predicted is the right type for producing stable, large-scale synchronization.
  • A computer model built from that response data accurately reproduced real firefly behavior across all eight tested blinking speeds, confirming that the measured rule is predictive rather than merely descriptive.
  • The same timing mechanism appears in brain cells and the body’s internal sleep-cycle clock, placing firefly light shows within a much broader biological framework for how living systems synchronize.

Every summer across the southeastern United States, thousands of fireflies put on a performance that looks almost choreographed, blinking their lights in near-perfect unison as if following some invisible conductor. For decades, scientists have marveled at how these tiny beetles, each with its own slightly different internal rhythm, manage to sync up so precisely. Now, a team of researchers has measured the hidden rule that makes it all work, and it turns out fireflies use the same basic trick that brain cells do.

The breakthrough came from fieldwork at Congaree National Park in South Carolina, where researchers spent five consecutive springs, from 2021 through 2025, running experiments on individual male fireflies of the species Photuris frontalis. What they discovered is that a firefly doesn’t simply speed up when it sees a neighbor’s flash. Depending on when in its own blinking cycle it spots another flash, a firefly will either speed up or slow down its next blink. That push-pull dynamic is what allows entire swarms to lock into a shared beat. It’s the same mechanism that keeps networks of brain cells firing in rhythm.

Before this study, no one had ever directly measured this response pattern in any North American firefly species. Researchers had long assumed fireflies were acting like metronomes on a shelf gradually clicking into sync but lacked the hard data to prove exactly how the adjustment worked. This pre-print paper fills that gap with the first experimentally measured timing rulebook for a North American firefly, describing how an individual tweaks its rhythm based on what it sees.

“For a whole season, I spent pretty much every night in the dark watching lights blink at a fixed frequency,” says lead author Owen Martin, who earned his doctorate in computer science from CU Boulder in 2025, in a statement. “Then, occasionally, I’d get this magical experience where I’d see the firefly just start syncing with the light. I would wonder if I was just seeing things.”

How Scientists Caught Fireflies in the Act

The experimental setup was simple, if labor-intensive. Researchers placed a single male firefly inside a small transparent cage within a larger tent that blocked out all outside light. Half a meter away, they positioned a single LED tuned to a yellowish-green color close to a real firefly flash. The LED was dimmed to roughly match the brightness of an actual firefly and was programmed to blink at a fixed rate using a small computer chip.

Each experiment began with a quiet observation period, averaging about 70 seconds, where the firefly was left alone to flash at its own natural pace. Then the LED was switched on, blinking steadily at one of eight different speeds. The firefly’s natural blinking speed fell somewhere in the middle of that range. A high-resolution camera recorded everything at 60 frames per second, and researchers later extracted the precise timing of every single flash, both from the firefly and the LED, frame by frame. Over the course of the study, they measured the behavior of 127 individual fireflies this way.

The central measurement was straightforward: how did the firefly’s blinking speed change depending on where in its own cycle it happened to see the LED flash? If the LED blinked right after the firefly had just flashed, early in the firefly’s cycle, the firefly typically slowed down, delaying its next flash. But if the LED blinked just before the firefly was about to flash anyway, late in its cycle, the firefly sped up, firing its next flash sooner than it otherwise would have. This two-directional response, speeding up in some situations and slowing down in others, is the kind that’s especially good at producing stable synchronization.

Long-exposure photo of a firefly swarm in Congaree
Long-exposure photo of a firefly swarm in Congaree. (Credit: Nolan Bonnie)

The Push and Pull Behind Firefly Synchronization

When the LED’s blinking speed was close to a firefly’s natural rhythm, the firefly adjusted most strongly, locking its flashes to match the LED. When the LED blinked much faster or slower than the firefly’s natural pace, the insects showed subtler but still measurable responses. Some fireflies matched every other LED blink or blinked twice for every one LED flash, essentially creative workarounds that let the firefly stay partially in sync even when the driving signal was far from its comfort zone.

The strongest adjustments happened when an external flash arrived at the most sensitive moments in a firefly’s cycle, right after it had just blinked or right before it was about to. Near those moments, the tug to speed up or the drag to slow down was at its peak. In the middle of the cycle, the firefly was relatively unfazed.

Armed with this behavioral data, the team built a computer model, a virtual firefly governed by the measured response rules. They programmed the virtual firefly to adjust its timing according to the same curve they’d measured in the field, then ran simulations mimicking each of the eight LED speed conditions. The simulated firefly’s blinking patterns closely matched what real fireflies actually did. The model captured not just the average behavior but the full spread of timing variations, including those creative workaround responses.

The researchers tested their model by sweeping through different values of its settings: how sharply the firefly responded to a stimulus, how strong the response was, and exactly when in the cycle the sensitive window fell. Each setting shaped the outcome in distinct ways. Sharpness controlled how tightly the firefly locked onto the external signal. Strength affected how wide the range of blinking speeds became. And shifting the sensitive window changed the balance between speeding up and slowing down. When all settings were tuned to match the experimental data, the model’s predictions aligned well with reality across all eight LED conditions tested.

What Firefly Light Shows Reveal About Synchronization in Nature

The response curve measured in single fireflies is, in effect, the interaction rule that governs the entire swarm. Previous theoretical work by mathematicians had shown that oscillators with this kind of two-way response, capable of both advancing and delaying, are exactly the type that can achieve stable, synchronized rhythms. Oscillators that can only speed up tend to be poor synchronizers. The firefly data now confirms that real fireflies possess the right kind of response for reliable synchronization, backing up from the ground level what theorists had predicted.

This same framework appears across biology. Brain cells synchronize their electrical firing using similar push-pull dynamics. Internal body clocks, the timekeepers that regulate sleep cycles, adjust to light exposure through comparable mechanisms. By showing that fireflies operate on the same principles, this work places one of nature’s most visible synchronized displays into a much larger scientific context.

Firefly
Researchers say the firefly discovery could help lead to improve drone functionality. (© soupstock – stock.adobe.com)

Looking ahead, the team suggests several promising directions. Temperature likely affects how fast fireflies blink without necessarily destroying their ability to synchronize, a prediction that could be tested under controlled conditions. The measured response curve could also be embedded into models of real swarm environments, where trees and terrain block lines of sight between fireflies, to predict when and where synchronization might break down into patchy, wave-like patterns.

“Peer-peer optical communication can be lower power and more secure, resulting more efficient swarming and robust aggregations despite requiring line-of-sight, adding a complementary capability to today’s miniature SWAP-constrained drones which largely rely on radio frequency-based approaches,” suggests co-author Kaushik Jayaram, an engineer at Imperial College London, in a statement.

“If you’re trying to get a lot of robots to push a large object, and they’re pushing at different times, then they’re going to struggle,” she adds. “But if they’re all pushing at the same time, they’ll be a lot more successful.”

Perhaps most intriguingly, comparing response curves across species could reveal why some fireflies synchronize while others don’t, and whether synchronizing species have evolved a specific kind of response to avoid being fooled by predatory fireflies that mimic their signals.

“This research opens the door to discovering other examples of synchronization in nature that we haven’t seen yet,” says Martin.

After five years of catching fireflies one at a time, sealing them in dark tents, and flashing LEDs at them with careful timing, the researchers have distilled one of nature’s great collective mysteries into a single curve on a graph. Each firefly carries a simple rule: speed up or slow down depending on when a flash arrives. From that rule, thousands of individuals weave a light show that has captivated humans for generations. The magic, it turns out, is in the math.

Paper Notes

Limitations

This study measured the timing response of individual male Photuris frontalis fireflies responding to a single LED stimulus, not to other live fireflies. The experimental setting isolated each firefly from all external light and social context, meaning the results reflect one-on-one interactions with an artificial signal rather than the multi-directional visual environment of a real swarm. Fireflies did not flash on every LED cycle, and some individuals ignored the LED entirely, requiring the researchers to filter the data for periods of active engagement. Individual flash timing was naturally variable both within and across individuals, introducing noise that the researchers addressed through windowed analysis and bout segmentation. The study examined only male fireflies and only one species. The model, while effective at reproducing observed patterns, uses a simplified framework that does not account for the underlying brain or chemical mechanisms driving flash production. The paper is a preprint that has not yet undergone certified peer review.

Funding and Disclosures

The provided content does not include specific funding sources, grant numbers, or conflict-of-interest disclosures.

Publication Details

Title: Excitation–inhibition interactions mediate firefly flash synchronization | Authors: Owen Martin, Nataliya Nechyporenko, Kaushik Jayaram, and Orit Peleg. Affiliations include the Department of Computer Science, Department of Mechanical Engineering, and BioFrontiers Institute at the University of Colorado Boulder; the Department of Bioengineering at Imperial College London; the Departments of Physics, Applied Math, and Ecology and Evolutionary Biology at the University of Colorado Boulder; and the Santa Fe Institute. | Journal/Platform: bioRxiv preprint (not certified by peer review | DOI: https://doi.org/10.64898/2026.01.19.700439 | Posted: January 20, 2026 | License: CC-BY-NC 4.0 International | Corresponding authors: Owen Martin ([email protected]) and Orit Peleg ([email protected])

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