Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics in in vitro neuronal networks

In vitro models of neural tissues are crucial to gain new insights on the pathophysiology of the brain. However, cell cultures are associated with many drawbacks and difficulties, e.g., technical complexities, ethical problems and high cost. Computational model-based solutions could represent an important tool to support the study of neuron function. In this work we present a novel computational platform where digital neuronal networks, i.e., Digitoids, can be developed with different size and layouts. The Digitoids rely on a novel firing model where the dependence on oxygen concentration is introduced, since it is a crucial limiting factor in cell cultures. To validate the performance of the platform, Digitoids were developed with the same morphological arrangement as observed in neuron monolayers in vitro. The comparison between the functional output of the Digitoids and the experimental data are not statistically different. The platform delivers a flexible digital tool that can easily be adapted for mimicking in vitro models with increasing complexity and can be exploited to optimize the laboratory experiments involving neuron cultures. Author SummaryThe use of cell models within laboratories is crucial to gain new understandings of the functioning of neuronal assemblies. However, culturing cells requires highly skilled personnel, a huge quantity of disposable materials and is thus associated with elevated costs. To overcome these limitations, the use of computer-based systems that reproduce the electrical behaviour of neurons can be employed. Since in vitro models are vessel-free, we present a novel computational model of neuron electrophysiology, where we introduce the dependence on local oxygen concentration Indeed, oxygen affects the metabolism and the function of cultured neurons. Thanks to our model and platform, we are able to reproduce the morphology of the cultured networks of neurons and build the so-called Digitoids. We then simulate the Digitoids and obtain their electrophysiological output. We compared the Digitoids output with experimental data from neurons cultured on microelectrode arrays. We did not find any differences between the electrophysiological output of the Digitoids and the experimental data, therefore we conclude that our novel oxygen-dependent model can be used to develop more physiologically relevant tools for simulating the activity of cultured neurons.