BRAINCELL modelling platform for stochastic nanoscale organisation and dynamic extracellular signalling among neurons and glia

The brain is a vast, wet city of cells, where information travels not just along wires, but through a complex, shifting soup that surrounds them. For decades, scientists have built computer models to understand how these cells, called neurons, talk to one another. These models have been incredibly useful, allowing researchers to simulate how electrical signals move through the intricate branches of a single nerve cell. However, traditional models have often treated the brain as a static place. They assumed the space between cells was empty and unchanging, and they simplified the tiny, complex structures that cover the surface of neurons, often ignoring them entirely to save computer power. This simplification meant that while the models could show how a signal travels, they might miss how the environment itself changes the signal, or how the tiny, microscopic details of a cell's shape alter its behavior.

A new simulation platform called BRAINCELL aims to fix these gaps by creating a much more realistic digital world. Instead of treating the space between cells as a void, this system models it as a dynamic fluid where ions and chemical messengers move, accumulate, and clear away in real time. It also fills the surfaces of neurons with thousands of tiny, realistic bumps and branches that exist in the real brain but were previously too difficult to simulate. By combining these two features—a dynamic environment and a detailed, bumpy cell structure—the researchers can now watch how brain cells behave in conditions that closely mimic life, revealing that the old, simplified models often told a different story than the new, detailed ones.

The researchers began by tackling the problem of dendritic spines. These are tiny, mushroom-shaped protrusions that cover the branches of neurons, acting as the primary receiving stations for signals from other cells. In the real brain, a single neuron can have thousands of these spines, but they are so small and numerous that most computer models simply leave them out. Using BRAINCELL, the team generated a population of these spines based on real measurements taken from living tissue, populating a model of a hippocampal neuron with hundreds of them. They then tested how this neuron responded to electrical signals compared to a version of the same cell without any spines. The results were striking. The presence of the spines did not just add a little detail; it fundamentally changed how the cell processed information. The neuron with spines could handle a much wider range of signal speeds, effectively doubling the frequency of inputs it could manage without getting overwhelmed. This suggests that the tiny, bumpy surface of a real neuron is not just decoration, but a critical feature that determines how the brain processes the flood of information it receives every second.

The team also discovered that the location of these signals matters just as much as their strength. In previous models, scientists often assumed that the likelihood of a signal being sent from one cell to another was the same everywhere along a neuron's branch. BRAINCELL allowed the researchers to test what happens when this probability varies, creating a gradient where signals are more likely to be released at the far ends of the branches than near the center. When they simulated this uneven distribution, the neuron's ability to integrate signals changed significantly. The model showed that the specific pattern of where signals are released acts like a tuning knob for the circuit, allowing the brain to adjust how it processes information without changing the total number of connections. This finding implies that the brain might use these subtle spatial patterns to fine-tune its operations in ways that were previously invisible to simpler models.

Beyond the shape of the cells, the new platform shines a light on the fluid that surrounds them. In the real brain, when neurons fire, they release potassium, a charged particle that builds up in the tiny gaps between cells. Traditional models often ignored this buildup or treated it as a constant background level. BRAINCELL, however, tracks the movement of these ions as they diffuse and are cleared away. The simulations showed that even a small, temporary rise in potassium concentration could make a neuron much more excitable, causing it to fire more easily. This dynamic environment means that a neuron's sensitivity is not fixed; it shifts moment by moment based on the chemical history of its immediate surroundings. The researchers found that this effect was particularly strong at low frequencies of activity, suggesting that the brain's responsiveness is deeply tied to the ebb and flow of ions in the space between cells.

This dynamic view of the environment also changed how the team understood communication between different types of cells. They modeled a scenario where an inhibitory neuron, which acts as a brake on brain activity, released a chemical called GABA. In the real brain, this chemical can drift far from its release point, creating a slow-moving wave that inhibits many neighboring cells at once, a process known as volume transmission. By simulating the diffusion of GABA through the extracellular space, the researchers showed that a single active neuron could generate a wave of inhibition that reduced the firing rate of a nearby main neuron by nearly half. This was a much stronger effect than what would be predicted by looking only at the direct, wired connections between the two cells. The simulation revealed that the brain relies heavily on this chemical "fog" to regulate activity, a mechanism that is lost when the environment is treated as empty space.

The platform also allowed the researchers to explore the role of astrocytes, a type of support cell that wraps around neurons and helps maintain the chemical balance of the brain. These cells have incredibly fine, hair-like branches that reach into the smallest gaps between neurons. The researchers built models that included these nanoscopic branches and found that their specific shape and density controlled how calcium signals moved inside the astrocyte. Furthermore, they showed that these cells act as a buffer for potassium. When they simulated a group of astrocytes surrounding a firing neuron, the cells absorbed the excess potassium released by the neuron, which in turn lowered the neuron's firing rate. This demonstrated that a small group of support cells can actively dampen the activity of a neuron simply by managing the chemical soup around it, a form of communication that happens without any direct electrical connection.

Perhaps the most surprising application of this new approach involved the myelin sheath, the fatty insulation that wraps around nerve fibers to speed up signals. For a long time, scientists have modeled myelin as a passive electrical insulator, like the plastic coating on a wire. BRAINCELL treated the myelin as a living part of the cell, populated with its own ion channels that actively manage potassium. When the researchers simulated a nerve fiber firing at high speeds, they found that if these channels were removed, the potassium released by the nerve would build up under the myelin sheath. This buildup eventually caused the signal to fail, stopping the nerve impulse from traveling. The simulation suggested that the myelin sheath is not just a passive wrapper but an active participant in keeping nerve signals running smoothly, and that its failure to manage ions could be a key factor in how nerve signals break down.

Finally, the researchers demonstrated how this platform could be used to model immune cells in the brain, known as microglia. They built a model of a microglial cell and introduced a source of a chemical signal called ATP nearby. The simulation showed how the chemical diffused through the space and triggered a response in the cell, with the intensity of the response varying depending on how close different parts of the cell were to the source. This highlighted how the shape of the cell and the movement of chemicals in the surrounding space combine to create a complex, uneven reaction across the cell's surface.

The work presented in this paper does not claim to have solved every mystery of the brain, but it provides a powerful new lens through which to view it. By moving away from static, simplified models and embracing the messy, dynamic reality of the brain's environment and structure, the researchers have shown that many of the rules governing brain activity are more complex than previously thought. The findings suggest that the tiny details of cell shape and the constant chemical shifts in the space between cells are not minor footnotes, but central players in how the brain thinks, learns, and responds to the world. The platform itself is now available for other scientists to use, offering a way to test these new ideas and explore the brain in a way that was previously impossible.