From virtual brains to simulated universes, computer simulation is transforming how we explore reality's deepest mysteries
Imagine if our most profound scientific questions—from the birth of a galaxy to the firing of a single neuron—could be explored not in a laboratory, but inside a computer.
What if we could test theories about climate change, disease spread, or even the fundamental nature of reality itself by creating virtual worlds that obey precisely defined scientific rules? This is no longer the realm of science fiction. Across the Nordic countries and beyond, a quiet revolution is underway, one that is transforming how we conduct science and understand our universe.
This revolution centers on computer simulation, a powerful methodology that has become indispensable across physics, chemistry, biology, and mathematics. From revealing the brain's decision-making secrets to suggesting that gravity itself might be a data compression algorithm in a cosmic computer, simulations are pushing the boundaries of knowledge. At the forefront of this movement, the Third Nordic Symposium on Computer Simulation brings together brilliant minds exploring these digital frontiers, sharing breakthroughs that are reshaping our understanding of everything from the infinitesimally small to the cosmologically large.
At its core, computer simulation is a step-by-step computational method for exploring the approximate behavior of mathematical models that represent real-world systems 9 . When scientists cannot solve a system's equations analytically—or when conducting real-world experiments is too dangerous, expensive, or ethically challenging—they turn to simulation. By converting scientific theories into algorithms that run on powerful computers, researchers can create virtual laboratories where they can observe phenomena otherwise hidden from view.
The philosophy behind this approach is both simple and profound. As one definition states, a simulation "imitates one process by another process," where this term "refers solely to some object or system whose state changes in time" 9 .
In practice, this means creating a digital twin of the system under study—whether it's a protein folding, a galaxy forming, or a disease spreading through a population.
The process typically follows a structured pathway. First, researchers develop a mathematical model based on established theory. This model is then translated into computer code using programming languages and specialized software. When executed, the simulation generates vast amounts of data representing the system's behavior over time, which scientists analyze and visualize to draw conclusions about the real-world phenomena they're studying 9 .
Develop theoretical framework based on established scientific principles
Translate mathematical models into computational algorithms
Run simulations on powerful computing systems
Analyze and visualize generated data to draw scientific conclusions
This approach has evolved into what some call the "third pillar of science," standing alongside traditional theoretical and experimental approaches. The growth has been particularly remarkable in fields ranging from astrophysics and particle physics to materials science, engineering, fluid mechanics, climate science, evolutionary biology, ecology, economics, and sociology 9 . What unites these diverse applications is a shared recognition that some of nature's deepest mysteries yield their secrets more readily to digital experimentation than to physical observation alone.
To understand how simulation drives scientific discovery, let's examine a groundbreaking experiment from Tufts University School of Medicine and MIT that simulates how brain circuits make decisions 5 . This research addresses a fundamental question: how does our brain make thousands of daily decisions based on incomplete information, and what happens when this system malfunctions in conditions like schizophrenia or obsessive-compulsive disorder?
Biologically realistic neural network simulation
The research team developed CogLinks, an innovative computer model that simulates brain circuits with unprecedented biological realism 5 . Unlike many artificial intelligence systems that function as "black boxes," CogLinks was designed to mirror how real brain cells are connected and how they assign value to often ambiguous observations about the external environment.
They created a simulation that replicates the neural pathways between key brain regions involved in decision-making, particularly the prefrontal cortex (responsible for planning) and the mediodorsal thalamus (a deeper brain region acting as a switchboard).
Like a "flight simulator for the brain," CogLinks allowed the team to test what happens when specific neural connections are weakened or altered 5 .
The team conducted a companion fMRI study where volunteers played a game in which the rules unexpectedly changed, allowing them to verify if their simulation predictions held true in actual human brains.
The simulation revealed a crucial insight: when the virtual connection between the prefrontal cortex and mediodorsal thalamus was weakened, the system defaulted to slower, habit-driven learning 5 . This suggested that this specific neural pathway is essential for mental flexibility and adaptability.
The subsequent human experiments confirmed what the model had predicted. Brain imaging showed that the mediodorsal thalamus indeed acts as a critical switchboard, linking the brain's two main learning systems—the flexible system (for adapting to new situations) and the habitual system (for routine behaviors) 5 . This region becomes particularly active when we recognize that circumstances have changed and we need to adjust our strategy.
Michael Halassa, the senior author of the study, envisions this approach leading to a new field of "algorithmic psychiatry," where computer models help reveal how mental illness emerges from changes in brain circuits, potentially identifying biological markers for precisely targeted treatments 5 .
Thalamus-prefrontal connection enables adaptive decision-making and explains how the brain switches between flexible and habitual thinking.
| Research Aspect | Description | Significance |
|---|---|---|
| Simulation Model | CogLinks - biologically realistic neural network | Mirrors actual brain connectivity and function |
| Key Brain Regions | Prefrontal cortex and mediodorsal thalamus | Identified critical pathway for cognitive flexibility |
| Experimental Approach | Virtual manipulation followed by human fMRI validation | Combined simulation predictions with real-world testing |
| Primary Finding | Thalamus-prefrontal connection enables adaptive decision-making | Explains how brain switches between flexible and habitual thinking |
This research exemplifies the power of computer simulation to illuminate complex biological processes that would be difficult or impossible to observe directly. By creating a virtual model of the brain's decision-making apparatus, scientists could run controlled experiments that revealed the specific circuits responsible for cognitive flexibility—with profound implications for understanding and treating psychiatric disorders.
While simulations help us understand specific biological processes, some scientists are pursuing an even more ambitious question: Could our entire universe be a simulation? Though it sounds like science fiction, this idea is being seriously explored by physicists using the very tools of computer simulation.
Dr. Melvin Vopson, an associate professor in physics at the University of Portsmouth in the UK, has proposed a revolutionary theory suggesting that gravity itself may be evidence we're living in a virtual simulation 1 . In a recent paper published in AIP Advances, Vopson argues that gravity isn't just a fundamental "pull" between masses—it's what happens when the universe is trying to keep its data organized.
Vopson's theory builds on the concept of information entropy—essentially a measure of how much information exists in an object or system 1 . He proposes that gravitational attraction helps reduce information entropy, with the universe operating like an enormous computer that compresses and optimizes data.
"Forcing objects with mass to be pulled toward the Earth's core is similar to how computers compress code," Vopson claimed in his paper 1 . He suggests that "the universe evolves in a way that the information content in it is compressed, optimized and organized—just as computers and computer code do" 1 .
This perspective fundamentally reframes our understanding of gravity. Rather than being a fundamental force, it becomes a process of data compression in a possibly simulated universe. As Vopson explains, "To put it simply, it is easier to compute all the properties and characteristics of a single object in space, rather than multiple objects. That is why objects in space are pulled together" 1 .
| Observation | Traditional Interpretation | Simulation Hypothesis Interpretation |
|---|---|---|
| Gravitational attraction | Fundamental force between masses | Universe optimizing data by consolidating objects |
| Formation of galaxies and planetary systems | Result of gravitational forces | Computational efficiency through data organization |
| Universal evolution | Governed by physical laws | Process of information compression and optimization |
| Mathematical symmetries in physics | Fundamental properties of nature | Built-in efficiencies in cosmic programming |
Across multiple scientific domains, researchers are leveraging an expanding arsenal of simulation tools to push the boundaries of knowledge. These digital laboratories are becoming increasingly sophisticated, accessible, and vital to scientific progress.
In medical education, Virtual Simulation Experiments (VSE) are transforming how students learn complex procedures. A 2025 study on using VSE for isolating, culturing, and identifying enteric pathogens found that students trained through virtual simulations achieved significantly higher scores in both operational skills (89.6 vs. 81.8) and knowledge retention (87.4 vs. 81.8) compared to those taught through traditional methods 3 .
The success of these virtual labs stems from their ability to provide immersive, risk-free learning environments where students can repeatedly practice procedures without real-world consequences.
"VSE's immersive environment and instant feedback enhanced satisfaction and autonomy" 3 , aligning with psychological principles of effective learning.
In materials science, researchers have access to an extensive array of specialized simulation software. This tools enable scientists to model everything from molecular dynamics to crystalline structures and chemical reactions.
| Software Name | Primary Function | License Type |
|---|---|---|
| GROMACS | Molecular dynamics | Free, open source |
| LAMMPS | Molecular dynamics | Free (GNU license) |
| HOOMD | Molecular dynamics | Free, open source |
| VASP | Ab initio molecular dynamics | Commercial |
| Gaussian | Electronic structure | Commercial |
| Avogadro | Molecule visualization | Free |
| VMD | Molecular dynamics visualization | Free |
| Quantum ESPRESSO | Electronic structure | Free, open source |
As we've seen from neuroscience to fundamental physics, computer simulation has evolved from a specialized tool into a transformative methodology that is reshaping entire scientific disciplines. The work presented at forums like the Third Nordic Symposium on Computer Simulation represents not just incremental progress but a fundamental shift in how we generate knowledge about our world.
These digital approaches allow us to explore questions that were previously unanswerable: What happens in the split-second when a brain circuit misfires? How would a new material behave under extreme conditions? Could our universe itself be an unimaginably sophisticated computation?
The future of simulated science points toward even greater integration and sophistication. We're moving toward "autonomous experimentation systems" where humans and robots partner to "advance science and technology orders of magnitude faster than humans do today" 4 .
What makes this field particularly exciting is its inherently interdisciplinary nature. The same simulation techniques that model protein folding can be adapted to study galaxy formation or climate patterns. This cross-pollination of ideas and methods suggests that the convergence of computer simulation with artificial intelligence, high-performance computing, and data science will continue to yield astonishing insights into the workings of our universe—whether natural or simulated.
As these digital laboratories become increasingly sophisticated, they remind us of the boundless creativity of the human mind—capable of building virtual worlds to better understand the physical one we inhabit. The simulation revolution in science is just beginning, and its ultimate implications may be as profound as any discovery in the history of human inquiry.