TECH

Secrets of the brain: how computers are helping scientists unravel the organ's mysteries

For decades, neuroscientists have sought to understand how neurons generate thoughts, memories, and emotions. Recent advances in computer simulations, artificial intelligence, and mathematical models have revolutionized this field of knowledge. Known as computational neuroscience, this discipline is expanding our theoretical understanding of how the brain processes information.

It studies everything from the electrical and chemical mechanisms that make a single neuron function to complex networks of millions of neurons working in unison. It also links biological processes to cognitive functions, such as learning and decision-making. This enables the creation of digital simulations of brain regions and the testing of hypotheses that could not be investigated directly in a laboratory setting.

Consequently, computational neuroscience has paved the way for new technologies capable of, for instance, predicting epileptic seizures and restoring movement in paralyzed individuals. Furthermore, it inspires new breakthroughs in computing, artificial intelligence, robotics, and intelligent systems.

A complex system...The human brain is composed of billions of neurons interconnected by trillions of synapses. Each neuron receives signals (both electrical and chemical) from hundreds of others simultaneously. When these signals accumulate and cross a certain threshold, the neuron fires an electrical impulse to transmit its message. This communication occurs at the synapses: the sending neuron releases neurotransmitters that fit into receptors like a key in a lock.

One of the most fascinating characteristics of this system is its ability to reorganize itself. Frequently used connections strengthen, while inactive ones weaken. This mechanism—known as synaptic plasticity—is what allows us to learn and form memories. Although individual neurons perform relatively simple tasks, their collective interaction gives rise to sophisticated behaviors, such as recognizing faces, experiencing fear, and planning a trip. Simulated brains... To study all this complexity, computational neuroscientists create digital simulators that mimic the behavior of neurons and neural networks, supported by new software architectures. Their starting point was the groundbreaking work of British physiologists Alan Hodgkin and Andrew Huxley; in 1952, they mathematically described how a neuron fires an electrical signal—work that earned them a Nobel Prize and remains the foundation of the field to this day.

One of the most widely used digital neuron simulation models today is known as "integrate-and-fire." In this model, the neuron functions like a bucket being filled with drops of water. Each incoming signal adds a few drops. When the level exceeds a certain threshold, the bucket tips over and empties, firing its contents to other neurons. The process then begins anew. This model is computationally efficient, as it allows for the simulation of thousands of neurons simultaneously.

One of the most ambitious research initiatives in this field today is the Blue Brain Project in Switzerland, which seeks to digitally reconstruct sections of the rat cortex with immense biological detail. Even so, the result still represents only a tiny fraction of what a real brain does.

Simulating the 86 billion human neurons and their 100 trillion connections remains far beyond the capabilities of current technology. Consequently, scientists work with varying levels of abstraction, selecting the appropriate level of detail needed to answer each specific question that arises.

The inspiration for modern AI...The relationship between computational neuroscience and artificial intelligence is a two-way street. Back in the 1940s, neurophysiologist Warren McCulloch and cognitive scientist Walter Pitts created the first abstract model of an artificial neuron, directly inspired by the biological neuron.

Decades later, by stacking layers of these artificial units, scientists developed computational models of artificial neural networks. This is the foundation of deep learning, which drives technologies such as speech recognition, medical imaging, and language models.

The comparison also revealed significant differences: the brain learns from very few examples and consumes about 20 watts—less than a light bulb. In contrast, training a large AI model can consume as much energy as hundreds of households over the course of several days. Understanding how the brain achieves such efficiency remains one of the field's major questions. The answer could transform the way we build artificial intelligence.

Healthcare applications...In practice, this research is already changing lives. In experiments using brain-computer interfaces (i.e., with sensors implanted in the brain), completely paralyzed individuals have managed to control robotic arms simply by thinking about moving. Although the movements lack speed and precision, they demonstrate that regaining function is feasible, even years after a central nervous system injury.

Some computational models can already identify brain patterns signaling the onset of an epileptic seizure, providing minutes or even hours of advance warning. For those with drug-resistant epilepsy, such a warning can significantly improve their daily routine and autonomy.

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