doi.org/10.1103/PhysRevLett.134.068403
Credibility: 989
#Neurons
The brain is able to process information thanks to a complex network of connections between different types of neurons
One of the main goals of neuroscience is to understand how these connections affect the way the brain works with the information it receives.
Researchers from the University of Padua, the Max Planck Institute for Physics of Complex Systems and the École Polytechnique Fédérale de Lausanne conducted a study to investigate how excitatory neurons (which stimulate activity) and inhibitory neurons (which reduce activity) contribute to the brain’s encoding of information.
The results, published in the journal Physical Review Letters, show that information processing works best when there is a balance between these two types of neurons.
“Our study began with a basic question in neuroscience: how does the structure of the brain influence its ability to process information”” explained Giacomo Barzon, one of the authors of the article, in an interview with Medical Xpress.
“The brain is constantly receiving and gathering information from the senses, and neurons don’t work alone-they’re part of complex networks that are constantly connecting.
One thing that’s striking about these networks is the balance between excitatory and inhibitory neurons, something that’s been observed in many parts of the brain.” The main goal of Barzon and his colleagues’ study was to find out whether this balance between the two types of neurons goes beyond simply keeping the brain’s activity stable.
They wanted to know whether this balance also helps the brain process information more efficiently.
“We were inspired by previous studies, both experimental and theoretical, that have highlighted the importance of this balance.
We looked at a model that shows how these two groups of neurons interact and studied-with calculations and simulations-how they respond to external signals,” said Daniel M.
Busiello, another author of the study.
“Using tools from information theory, we discovered something interesting: Networks of neurons that are good at encoding information over long periods of time may not be as quick to respond to rapid changes in the signals they receive.” Using mathematical and theoretical approaches, the researchers showed that information processing is most effective when the brain is at a critical point of stability-that is, when the activity of excitatory and inhibitory neurons is well balanced.
These results indicate that adjusting this balance not only keeps the brain functioning stably, but may also be essential for it to encode information in the best possible way.
“We were able to prove, from an information theory perspective, that interactions between excitation and inhibition are fundamental for groups of neurons to be able to encode information about external signals that change over time,” said Giorgio Nicoletti, also an author of the study.
“This is especially interesting because we already know that this balance is important for controlling the activity of neurons.
Our method allows us to measure this effect in terms of information as something concrete.”
This recent work by Barzon, Busiello, and Nicoletti could open new doors to understanding how the brain processes information and what mechanisms are behind it.
In future studies, the researchers plan to expand on these results by applying the same approach to analyze more complicated connection structures in the brain.
“Furthermore, in real neural networks, connections are not fixed-they change over time, influenced by both external stimuli and the network’s own activity,” Barzon added.
“This dynamic nature of connections could play an important role in how neurons process and encode information.
This could help us understand how learning and adaptability affect how the brain works with information.”
Published in 03/12/2025 13h07
Text adapted by AI (ChatGPT / Gemini) and translated via Google API in the English version. Images from public image libraries or credits in the caption. Information about DOI, author and institution can be found in the body of the article.
Reference article:
Original study:
| Geoprocessing Drone Systems HPC |
| ERP and CRM Systems Mobile Systems AI |