Scientists at Germany’s Jülich Research Centre are taking a bold step: they’re preparing to simulate the human brain on a supercomputer. Building on recent breakthroughs—like the first full map of a fruit fly’s brain circuitry in 2024—this work aims to bring us closer to understanding how around 86 billion neurons in the human brain work together to produce thought, memory, and consciousness. This isn’t science fiction, and it’s not a promise of “digital immortality” either—it’s a careful, methodical effort at the frontiers of neuroscience and high‑performance computing.


Visualization of a human brain overlaid with supercomputer circuitry
Visualization of brain-like neural networks running on a supercomputer. Image credit: Futurism / Jülich-related coverage.

Why Simulating the Human Brain Matters

The goal isn’t to “replace” human brains, but to build powerful models that help researchers:

  • Test ideas about how thoughts, memories, and emotions emerge from physical neurons.
  • Explore new approaches to treating neurological conditions like epilepsy, Alzheimer’s disease, and depression.
  • Design more efficient AI systems inspired by biological brains rather than traditional computer chips.
“Large-scale brain simulations are not a shortcut to understanding the mind, but they are one of the most powerful tools we have to connect biology, computation, and behavior.”
— Computational neuroscientist quoted in recent review literature

From Fruit Fly to Human Brain: A Giant Leap in Complexity

In 2024, researchers completed the first-ever detailed wiring map—called a connectome—of the fruit fly brain. Despite being tiny, the fly brain:

  • Contains roughly 130,000 neurons.
  • Includes about 54.5 million synapses—the connections between neurons.
  • Packs nearly 500 feet of neural “wiring” into a space smaller than a grain of sand.

Mapping and modeling this miniature brain required massive computing power and years of effort. Yet compared to the human brain—with an estimated 86 billion neurons and up to a quadrillion synapses—the fruit fly is only a stepping stone.

The humble fruit fly, whose fully mapped brain connectome is a crucial stepping stone toward human brain simulations. Image: Wikimedia Commons (CC BY-SA).

Inside the Jülich Supercomputer Effort

The Jülich Research Centre in Germany is home to some of the world’s most powerful supercomputers. Researchers there, in collaboration with European partners, are working toward simulations that operate at the scale of the entire human brain—not in perfect biological detail, but at resolutions fine enough to explore realistic network behavior.

Their work builds on initiatives like the Human Brain Project and ongoing projects in exascale computing (systems capable of at least one billion billion calculations per second).

  1. Massive parallel computing: Supercomputers distribute brain models across tens of thousands of processor cores working together.
  2. Advanced brain models: Software frameworks simulate neurons and synapses with varying levels of biological detail.
  3. Data from real brains: MRI scans, microscopy, and electrophysiology recordings help constrain and validate the models.
Rows of high-performance computing servers in a data center
High-performance computing clusters like those at Jülich make large-scale brain simulations possible. Image: Unsplash.
“We don’t expect a single, perfect ‘digital brain.’ Instead, we build multiple models at different levels of detail to answer specific scientific questions.”
— Paraphrased from public communications by European brain simulation teams

What Does It Actually Mean to Simulate a Human Brain?

“Simulating the human brain” can sound like copying a mind into a machine. In reality, current projects aim for something more modest and scientifically grounded:

  • Large-scale network models: Representing millions to billions of neurons and their connections in software.
  • Biophysically inspired behavior: Neurons in the model follow equations that approximate how electrical signals and chemicals behave in real brain cells.
  • Task-specific simulations: Focusing on particular brain regions (like visual cortex or hippocampus) or functions (like memory or decision-making).

These models can be run, paused, and tweaked—allowing researchers to ask “what if?” questions that would be impossible or unethical in living humans.


Potential Benefits: From Neurological Disease to Smarter AI

While whole-brain simulation is still in its early stages, researchers see several long-term benefits. None are guaranteed, but many are plausible based on current evidence.

1. Understanding and Treating Brain Disorders

Brain simulations could help scientists explore how changes in connectivity or cell function might lead to:

  • Epileptic seizures and abnormal electrical activity.
  • Memory loss patterns seen in Alzheimer’s disease.
  • Network-level changes associated with depression or schizophrenia.

In the future, this may support more targeted drug development or personalized treatment planning, though that vision is still years—if not decades—away.

2. Building Better Artificial Intelligence

Today’s AI systems, including large language models, are inspired only loosely by the brain. Detailed simulations could reveal new principles of:

  • Energy-efficient learning.
  • Robustness to damage and noise.
  • Flexible, general-purpose problem solving.

3. Fundamental Science: How Does Thought Arise?

Perhaps the most profound benefit is basic understanding. By comparing simulations with real brain recordings, scientists can test theories about how:

  • Perception and attention are coordinated.
  • Memories are encoded, stored, and retrieved.
  • Complex behaviors and decisions emerge from simple components.
Stylized representation of artificial neural networks resembling a brain
Insights from brain simulations could influence the next generation of AI architectures. Image: Unsplash.

Major Obstacles on the Road to Whole-Brain Simulation

Even with modern supercomputers, simulating a full human brain in rich biological detail is beyond our current capabilities. Key challenges include:

  • Data limitations: We don’t yet have a complete wiring diagram or cell-by-cell map of any mammalian brain at human scale.
  • Computational cost: Detailed models of even small brain regions can consume huge amounts of computing power and energy.
  • Biological complexity: Brains use chemistry, electricity, and dynamic structural changes. Capturing all of this faithfully is extremely hard.
  • Validation: It’s challenging to prove that a simulation truly behaves like a real brain region, rather than simply approximating some measurements.
Scientist working at multiple computer screens with data visualizations
Turning immense datasets into meaningful brain models is one of the field’s toughest challenges. Image: Unsplash.

Ethical and Societal Questions

As brain simulations grow in scale and realism, they raise challenging ethical questions—even if we are far away from conscious machines.

  • Data privacy: How should brain data from real patients and volunteers be stored, shared, and anonymized?
  • Dual-use concerns: Could advanced brain models be misused, for example in military contexts or manipulative technologies?
  • Concepts of identity and mind: How would society respond if simulations exhibit increasingly complex behavior that looks “mind-like”?
“We need ethics and governance to advance in step with our technical abilities, not several steps behind.”
— Common position in recent policy and neuroethics reports

A Glimpse from the Lab: How Simulations Help Today

To make this more concrete, consider a simplified, real-world style scenario based on published research patterns:

A team studying epilepsy builds a computer model of a small region of the cortex. They incorporate:

  • Neuron types and connectivity patterns measured from brain tissue slices.
  • Electrical properties derived from recordings in animal models.
  • Patterns of activity seen in patients’ EEG and fMRI scans.

By running the simulation, they can trigger seizure-like activity and experiment with:

  • Changing the strength of certain synapses.
  • Altering inhibitory versus excitatory balance.
  • Testing how different “virtual drugs” affect activity.

Findings from this kind of model don’t translate directly into treatments, but they can guide where to look next in animal studies or clinical trials, making the research pipeline more focused and efficient.


How You Can Follow and Understand This Rapidly Evolving Field

You don’t need a PhD in neuroscience to stay informed about brain simulation research. A few practical steps can help you track trustworthy developments without getting lost in hype.

  1. Start with reputable sources: Look for updates from major research centers and journals, such as:
  2. Watch for careful language: Be cautious of headlines that claim “full brain upload” or “digital immortality.” Responsible reporting usually highlights limitations as well as achievements.
  3. Learn the key terms: Understanding words like connectome, neural network, synapse, and exascale computing will make articles much easier to follow.
  4. Compare multiple sources: When a big announcement appears, see how different outlets cover it. If specialists and major journals are cautious, you should be too.

Looking Ahead: Curious, Hopeful, and Realistic

The effort at Jülich to simulate the human brain on a supercomputer is part of a much larger, international movement to understand our most complex organ. From the meticulous wiring map of a fruit fly’s brain to large-scale models of human brain networks, each advance is another piece of a puzzle that will likely take generations to assemble.

It’s reasonable to feel both excited and cautious. Excited, because these tools may eventually deepen our understanding of mental health, neurological disease, and even the nature of thought itself. Cautious, because the science is challenging, the timelines are long, and the ethical questions are serious.

For now, the most constructive thing we can do is stay informed, support rigorous and transparent research, and encourage policies that keep human values at the center of technological progress. The story of brain simulation is just beginning—and it’s one we’re all, in some way, a part of.

Call to action:

  • Bookmark a few reputable science outlets and check in regularly.
  • Share balanced, well-sourced articles on social media to counter sensationalism.
  • If you’re a student or educator, consider incorporating brain simulation topics into your studies or teaching—this is the frontier your generation will help shape.

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