The human brain is often described as the most complex and sophisticated information processing system in existence. Every second, it integrates an immense amount of sensory data, evaluates internal signals, and orchestrates finely-tuned responses that allow us to interact with the world. Underpinning this extraordinary ability is a highly organized and interconnected network of neurons—specialized cells that communicate through intricate electrochemical signals. A longstanding question in neuroscience has been how the structure and dynamics of these neural networks contribute to the brain’s remarkable capacity to encode and process information efficiently.
At the heart of this inquiry lies the balance between two fundamental types of neurons: excitatory and inhibitory. Excitatory neurons drive the brain’s activity forward by stimulating other neurons, effectively acting as accelerators in the system. Inhibitory neurons, on the other hand, apply the brakes—modulating and constraining neural activity to prevent excessive firing that could lead to instability or dysfunction. These two forces are locked in a dynamic and delicate equilibrium, a balance that scientists have long recognized as essential for maintaining healthy brain function. However, beyond its stabilizing role, many researchers have suspected that this balance might offer deeper insights into how the brain encodes and processes information.
A recent study conducted by researchers from the University of Padova, the Max Planck Institute for the Physics of Complex Systems, and the École Polytechnique Fédérale de Lausanne has shed new light on this question. Their findings, published in Physical Review Letters, reveal that the brain’s capacity to process information is maximized when there is an optimal balance between excitatory and inhibitory neural activity. This discovery offers a compelling perspective on how the brain’s architecture is fine-tuned not just for stability but also for optimal information flow.
The study was spearheaded by Giacomo Barzon, Daniel M. Busiello, and Giorgio Nicoletti, who brought together expertise in neuroscience, theoretical physics, and complex systems analysis. Their research was driven by a fundamental question: Does the intricate balance between excitation and inhibition in neural networks serve a purpose beyond simply preventing chaos? Specifically, they wanted to explore whether this balance could actually enhance the brain’s ability to process complex, time-varying information.
“The brain continuously receives and integrates sensory inputs, and neurons do not act in isolation—they are part of complex, recurrent networks,” said Giacomo Barzon in an interview with Medical Xpress. “One particularly intriguing feature of these networks is the balance between the activity of excitatory and inhibitory neurons, which has been observed across different brain regions.”
The team developed a mathematical model that captures the interactions between these two types of neurons and examined how this interaction influences the network’s ability to process external signals. Using tools from information theory, a branch of mathematics that studies the quantification and transmission of information, they analyzed how different configurations of excitation and inhibition impact the network’s performance.
Daniel M. Busiello explained that their analysis revealed a fascinating trade-off: neural networks optimized for accurately encoding information over long timescales tend to be less responsive to rapid changes in input. Conversely, networks that are highly sensitive to quick fluctuations may sacrifice the precision of information encoding over longer periods. This suggests that the brain’s ability to process information is constrained by a balance between stability and flexibility.
Their study showed that information processing is most effective when the system operates at the “edge of stability.” This critical state—where excitation and inhibition are finely balanced—allows neural networks to remain sensitive to incoming information while avoiding the extremes of chaos or silence. “We revealed a fundamental trade-off,” Busiello said. “Neural networks optimized for accurate encoding over long timescales may be less responsive to rapid changes in the input.”
One of the most significant outcomes of their research is the realization that the balance between excitation and inhibition doesn’t just keep neural activity stable; it actively shapes how well the brain can process time-varying information. Giorgio Nicoletti emphasized the novelty of this finding: “This is particularly interesting because excitation-inhibition balance is well-known to be a key ingredient in regulating neural activity. Our approach allows us to quantify such an effect in terms of information as a physical quantity.”
By framing the question through the lens of information theory, the researchers provided a more precise understanding of how neural populations encode external stimuli. Information, in this context, is not just an abstract concept but a measurable physical quantity that reflects how well the system can track and respond to changes in the environment.
These findings have far-reaching implications. The ability of the brain to balance excitation and inhibition has been observed across multiple species and brain regions, suggesting it is a universal principle of neural organization. Yet, until now, the reasons behind this ubiquitous balance have not been fully understood. Barzon and his team have taken a critical step toward explaining not just why this balance exists, but how it directly benefits the brain’s processing abilities.
Beyond the theoretical implications, the study opens up exciting new directions for future research. One of the key insights is that in real biological systems, neural connectivity is not static. Synaptic connections between neurons evolve over time, influenced by learning, experience, and external stimuli. This plasticity allows the brain to adapt and reorganize itself in response to new information and changing environments. Barzon highlighted this point by stating, “In real neural networks, connectivity is not static—it evolves over time, influenced by both external stimuli and internal network activity. This dynamic nature of connectivity might play a crucial role in shaping how neural populations process and encode information.”
Understanding how these adaptive changes affect the balance between excitation and inhibition could offer deeper insights into learning, memory formation, and even neurodevelopmental disorders. For instance, disruptions in the excitation-inhibition balance have been implicated in conditions such as autism spectrum disorder (ASD), schizophrenia, and epilepsy. These disorders often involve abnormal neural connectivity and irregular patterns of activity, leading to impairments in information processing and cognition. Insights from this study may eventually inform the development of therapies that aim to restore balance in these networks and improve cognitive function.
In the future, the researchers plan to extend their model to include more complex brain structures and network architectures. By incorporating features such as hierarchical connectivity, modular organization, and heterogeneous synaptic strengths, they hope to capture more realistic representations of brain networks. These advances could help answer additional questions about how large-scale brain dynamics emerge from the interactions of small neural populations and how learning mechanisms fine-tune these dynamics over time.
The findings from this study also resonate with broader questions in cognitive science and artificial intelligence. Many AI systems and machine learning algorithms are inspired by the structure and function of biological neural networks. Understanding how the brain optimizes information processing through excitation-inhibition balance may inspire new computational architectures that achieve similar efficiencies in data processing, adaptability, and learning.
At its core, this research underscores the beauty and complexity of the brain’s design. The balance between excitation and inhibition is not just a safety mechanism—it’s a finely-tuned feature that maximizes the brain’s ability to process information, adapt to new stimuli, and maintain stability in an ever-changing environment. The interplay of these forces allows the brain to walk a tightrope between chaos and order, giving rise to the remarkable abilities that define human cognition.
More information: Giacomo Barzon et al, Excitation-Inhibition Balance Controls Information Encoding in Neural Populations, Physical Review Letters (2025). DOI: 10.1103/PhysRevLett.134.068403.