Optimal brain processing requires balance between excitatory and inhibitory neurons, study suggests

The brain’s ability to process information is known to be supported by intricate connections between different neuron populations. A key objective of neuroscience research has been to delineate the processes via which these connections influence information processing.

Researchers at the University of Padova, the Max Planck Institute for the Physics of Complex Systems and École Polytechnique Fédérale de Lausanne recently carried out a study aimed at better understanding the contribution of excitatory and inhibitory neuron populations to the brain’s encoding of information. Their findings, published in Physical Review Letters, show that information processing is maximized when the activity of excitatory and inhibitory neurons is balanced.

“Our research was inspired by a fundamental question in neuroscience: how does the structure of the brain shape its ability to process information?” Giacomo Barzon, co-author of the paper, told Medical Xpress. “The brain continuously receives and integrates sensory inputs, and neurons do not act in isolation—they are part of complex, recurrent networks. 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 key objective of this recent study by Barzon and his colleagues was to determine whether the balance between excitatory and inhibitory neurons does more than stabilize neural activity. Specifically, the team explored the possibility that this balance also optimizes information processing.

“Inspired by several experimental and theoretical findings highlighting the importance of the balance between excitatory and inhibitory neurons, we analyzed a model that captures the interactions between these two populations and investigated—both analytically and numerically—their response to external signals,” explained Daniel M. Busiello, co-author of the paper. “Specifically, by employing tools of information theory, we revealed a fundamental trade-off: neural networks optimized for accurate encoding over long timescales may be less responsive to rapid changes in the input.”

Employing mathematical and theoretical approaches for studying information processing, the researchers showed that information processing is most effective at the edge of stability, a critical state in which the activity of excitatory and inhibitory neurons is balanced. Their results suggest that the fine-tuning of this excitation-inhibition balance could not only stabilize the brain’s activity, but could also play a crucial role in its ability to optimally encode information.

“In our study, we were able to show from an information-theoretic perspective that interactions between excitation and inhibition are crucial for allowing neural populations to encode information about time-varying external signals,” said Giorgio Nicoletti, co-author of the paper. “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.”

This recent work by Barzon, Busiello and Nicoletti could open new avenues for the study of information processing and its underlying neural mechanisms. In their next studies, the researchers plan to build on their results, using the same approach to study more complex brain connectivity structures.

“Moreover, in real neural networks, connectivity is not static—it evolves over time, influenced by both external stimuli and internal network activity,” added Barzon. “This dynamic nature of connectivity might play a crucial role in shaping how neural populations process and encode information, potentially offering insights into how learning and adaptive properties affect information encoding in neural systems.”

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