P016 Heterogeneous topologies in in silico networks are necessary to model the emergent dynamics of human-derived fully excitatory neuronal cultures

Valerio Barabino*, 1, Francesca Callegari1, Sergio Martinoia1, Paolo Massobrio1, 2

1Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
2National Institute for Nuclear Physics (INFN), Genova, Italy


* Email:valerio.barabino@edu.unige.it

Introduction
Murine neuronal cultures have been the gold standard forin vitromodels, but their outcome is not always translatable to the human brain, especially in personalized medicine. Human-induced pluripotent stem cells (hiPSCs) offer a promising alternative [1]. This model requires extensive characterization, andin vitromulti-electrode arrays (MEAs) recordings alone may not capture all relevant parameters. Computational modeling can complement these experiments, offering insight into the mechanisms behind peculiar electrophysiological activities or pathological conditions [2]. This work aims to infer the underlying mechanisms behind the emergent firing profile pattern in excitatory hiPSC neuronal networks coupled to MEAs [3].
Methods
We modeled 100 Hodgkin-Huxley neurons with short-term depressing synapses. To reproduce self-sustained spontaneous activity observedin vitro, we introduced noise and external DC currents to allow for the alternation of two phases: short periods of high-frequency firing involving the whole network and long periods of asynchronous low-frequency spiking. We explored the role of external triggers, the interplay between synaptic conductances (AMPA and NMDA) and synaptic depression, and network topology in recreating the averagein vitrocumulative firing pattern. To account for the heterogeneity of biological networks, we introduced different connectivity rules, distinguishing between incoming and outgoing links.
Results
Noise emerged as the best trigger for network bursts, allowing a good balance between random spiking and bursting activity with anin vitro-like variability of inter-network burst intervals. Lower AMPA conductance than NMDA was necessary, as NMDA ensured a broader operability range forin vitro-like activity. The optimal trade-off between NMDA contribution and synaptic depression was found near a transition state, implying that small parameter changes can shift the system into different regimes. To shape cumulative firing pattern profiles, heterogeneous topologies were introduced, distinguishing afferent and efferent connectivity. The mostin vitro-like profile arose from scale-free afferent and random efferent connections.
Discussion
Consistent with previous studies [4], our findings suggest that the nature ofin vitrohiPSC network bursts is governed by a mechanism of noise amplification, controlled by a pulse of activity that is randomly nucleated and propagates throughout the network. Regarding connectivity, scale-free for afferents implies that a small subset of “privileged” neurons receives most of the inputs (hubs), thus acting as central regulators and influencing the network’s overall activity. Notably, these hubs exhibited more tonic firing, effectively acting as pacemakers that initiate network bursts, as similarly identified in [5]. However, in our case this property is structural and not an intrinsic dynamic property of single neurons.




Acknowledgements
The authors thank dr. Giulia Parodi (University of Genova) for supplying the hiPSCs recordings. This work was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Project MNESYS (PE0000006)—A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022).
References
1.https://doi.org/10.1016/j.stemcr.2021.07.001
2.https://doi.org/10.1101/2024.05.23.595522
3.https://doi.org/10.1088/1741-2552/acf78b
4.https://doi.org/10.1038/nphys2686

5.https://doi.org/10.1007/s00422-010-0366-x