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PERSPECTIVE article

Front. Neurosci.

Sec. Neural Technology

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1638547

This article is part of the Research TopicNeuroengineering for health and disease: a multi-scale approachView all 9 articles

The road toward a physiological control of artificial respiration: the role of bio-inspired neuronal networks

Provisionally accepted
  • 1Dept of Biomedical, Metabolic and Neural Sciences, Universita degli Studi di Modena e Reggio Emilia, Modena, Italy
  • 2Dept of Engineering "Enzo Ferrari", Universita degli Studi di Modena e Reggio Emilia, Modena, Italy
  • 3Dept of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Modena, Italy

The final, formatted version of the article will be published soon.

The transition from mechanical to physiological ventilation is a delicate step during the recovery from ECMO, in particular following severe respiratory failures. Since there is controversy on the optimal degree of mechanical ventilation support, the maintenance of physiological ventilation can be crucial to determine the balance between lung rest and lung recovery. We believe that the development of closed-loop control systems for mechanical ventilation, designed to maintain or restore physiological respiratory activity in patients supported by extracorporeal membrane oxygenation (ECMO) could contribute to achieve this goal. In our vision, the core of such a system could be a biologically inspired computational model of the respiratory neural control center, capable of simulating the respiratory rhythm required to efficiently eliminate CO₂ from the body. The outputs of the modeled respiratory rhythm (e.g., rate and pattern) would represent the patient's needs that should be ideally maintained to ensure proper CO₂ clearance. The use of a simulated respiratory rhythm to dynamically control a mechanical ventilator integrated with ECMO would ensure that ventilatory support is adjusted in real time to meet the physiological demands indicated by inputs delivered by external sensors. One of the key advantages of this system would be its use during weaning from ECMO. By simulating a target respiratory rhythm and gradually transferring the workload from ECMO to mechanical ventilation, the system could allow for a smoother and safer transition to spontaneous or assisted breathing.

Keywords: Artificial breathing, computational neuroscience, Respiratory CPG, Neuronal simulation, real-time closed loop controls

Received: 30 May 2025; Accepted: 15 Aug 2025.

Copyright: © 2025 Perricone, Tartarini, De Toni, Rovati, Mapelli and Gandolfi. 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:
Jonathan Mapelli, Dept of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, 41125, Modena, Italy
Daniela Gandolfi, Dept of Engineering "Enzo Ferrari", Universita degli Studi di Modena e Reggio Emilia, Modena, Italy

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