Emergent locomotion from anatomy alone: a whole-connectome, conductance-based neuromechanical model of Caenorhabditis elegans

To understand how a living thing moves, scientists often look at two separate things: the wiring diagram of its brain and the physical mechanics of its body. The wiring diagram, known as a connectome, maps every single connection between neurons, much like a detailed map of a city's roads. The mechanics describe how muscles pull on a body to create motion. For decades, researchers have struggled to combine these two maps into a single, working model that can explain how a tiny animal decides to move forward, backward, or stop. The big question has been whether the raw blueprint of the nervous system, when given realistic electrical properties, is enough to generate complex behavior on its own, or if the brain needs a separate, pre-programmed rhythm to tell it how to move. This question touches on a deeper mystery in biology called degeneracy, which is the idea that many different internal settings can produce the exact same outward result. If this is true, then a nervous system might be incredibly robust, able to keep working even if individual parts change or fail.

A new study by Huan Li at Shanghai Liangta Biotech Co., Ltd. tackles this problem using the roundworm Caenorhabditis elegans, a creature so simple that its entire nervous system has been fully mapped. This worm has exactly 302 neurons, and scientists have already drawn a complete picture of how they connect. Li built a massive computer simulation that brings this entire nervous system to life. Instead of simplifying the neurons into basic on-off switches, the model treats every single one of the 292 neurons in the worm's body as a complex electrical unit with seventeen different types of ion channels. These channels act like tiny gates that control the flow of electricity and chemicals inside the cell. The model also includes the worm's body, which is simulated as a flexible rod that can bend, and it links the brain to the body so that the muscles can feel when the body bends and send that information back to the brain. Crucially, the researchers did not teach the model how to move. They did not adjust the numbers to make the worm walk correctly. They simply plugged in the known physical connections and the known electrical properties of the cells, then turned it on.

The result was a surprise. Without any training or pre-programmed rhythm, the simulation began to move on its own. The network of neurons, driven only by a steady, gentle electrical signal, generated a wave of bending that traveled from the worm's head to its tail. This wave pushed the virtual worm forward across the screen at a speed of 0.210 millimeters per second, with a wiggling frequency of 0.58 hertz. These numbers matched the movement of real worms almost perfectly. The study shows that the anatomy alone—the specific way the neurons are wired together, combined with their natural electrical behavior—is sufficient to create organized, realistic movement. The rhythm of the movement did not come from a central clock in the brain; it emerged naturally from the loop between the brain, the muscles, and the body's own sense of its position. This confirms that the worm's nervous system does not need a hidden instruction manual to walk; the instructions are already built into the physical structure of the connections.

The researchers then tested how fragile this system really is. They performed a series of virtual experiments where they turned off specific types of ion channels in every neuron at once, mimicking a genetic mutation. In some cases, these changes drastically altered the internal electrical state of the neurons, shifting their resting voltage by up to 12 millivolts and changing how strongly they fired by nearly 50 percent. Despite these massive internal changes, the worm's movement barely changed at all. The speed and frequency of the wiggling shifted by less than 0.3 percent. This finding provides strong evidence for the concept of degeneracy at the level of the whole animal. It suggests that the nervous system is designed with so much redundancy that it can absorb huge internal fluctuations without losing its ability to function. The behavior is protected not by any single channel, but by the collective ability of the entire network to compensate for changes in its parts.

However, the initial model had one small flaw: the worm's body bends were slightly too shallow compared to real worms. To fix this, the researchers added a single layer of chemical signaling that had been discovered in recent years but was missing from previous models. This system involves a specific neuron called DVA, which releases a chemical messenger that acts like a volume knob for the muscles. When the body bends, this neuron senses the stretch and boosts the signal sent to the muscles, making the bends deeper. When this single mechanism was added, the simulation's bending amplitude increased to match the biological range. This adjustment also allowed the model to correctly reproduce the behavior of real worms with genetic mutations in this system, which are known to move more slowly and with shallower bends. Furthermore, the model responded to a specific drug, aldicarb, in the same way real worms do. When the drug was introduced, the simulated worms with the intact chemical system sped up quickly, while those with the broken system did not, mirroring real-world experiments.

Finally, the team tested the model against specific experiments where scientists use light to turn individual neurons on and off. When they simulated turning on a neuron called AVA, the virtual worm immediately reversed direction and started crawling backward, just as real worms do. When they simulated turning on a neuron called RIS, which is associated with sleep, the worm stopped moving entirely and stayed still until the light was turned off. These results showed that the model captures the fundamental logic of the worm's behavior. The study concludes that a complete wiring diagram, when endowed with realistic electrical properties, is enough to generate complex behavior. It also highlights that while the wiring is the foundation, a specific layer of chemical modulation is necessary to fine-tune the movement to match the precision of a living animal. This work provides a new way to study how brains work, offering a tool where scientists can test how changes in genes or drugs might affect behavior before ever touching a living creature.