ORIGINAL RESEARCH article
Front. Neuroinform.
Volume 19 - 2025 | doi: 10.3389/fninf.2025.1549916
This article is part of the Research TopicWomen Pioneering Neuroinformatics and Neuroscience-Related Machine Learning, 2024View all 7 articles
Digitoids: a novel computational platform for mimicking oxygendependent firing of neurons in vitro
Provisionally accepted
- University of Pisa, Pisa, Italy
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Computational models are valuable tools for understanding and studying a wide range of characteristics and mechanisms of the brain. Furthermore, they can also be exploited to explore biological neural networks from neuronal culturesin vitro. However, few of the current in silico approaches consider the energetic demand of neurons to sustain their electrophysiological functions, specifically their wellknown oxygen-dependent firing. In this work, we introduce Digitoids, a computational platform which integrates a Hodgkin-Huxley-like model to describe the time-dependent oscillations of the neuronal membrane potential with oxygen dynamics in the culture environment. In Digitoids, neurons are connected to each other according to Small-World topologies observed in cell culturesvitro, and oxygen consumption by cells is modelled as limited by diffusion through the culture medium. The oxygen consumed is used to fuel their basal metabolism and the activity of Na + -K + -ATP membrane pumps, thus it modulates neuronal firing. Our simulations show that the characteristics of neuronal firing predicted throughout the network are related to oxygen availability. In addition, the average firing rate predicted by Digitoids is statistically similar to that measured in neuronal networks in vitro, further proving the relevance of this platform. Digitoids paves the way for a new generation of in silico models of neuronal networks, establishing the oxygen dependence of electrophysiological dynamics as a fundamental requirement to improve their physiological relevance.
Keywords: in silico Modelling, Neuron firing, oxygen metabolism, in vitro neuronal network, digitalized neuronal network
Received: 22 Dec 2024; Accepted: 09 Jun 2025.
Copyright: © 2025 Fabbri, Botte, Ahluwalia and Magliaro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chiara Magliaro, University of Pisa, Pisa, Italy
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