P234 Complexity of an astrocyte-neuron network model in random and hub-driven connectivity
Paolo Paradisi*1,2,Giulia Salzano3,Marco Cafiso1,4, Enrico Cataldo4
1ISTI-CNR-Institute of Information Science and Technologies “A. Faedo”, Pisa, Italy
2BCAM-Basque Center for Applied Mathematics, Bilbao, Basque Country, Spain
3Department of Neuroscience, International School for Advanced Studies, Trieste, Italy
4Department of Physics, University of Pisa, Pisa, Italy
Introduction
The role of glial cells, particularly astrocytes, in brain neural networks has been historically
overlooked due to a neuron-centric perspective. Recent research highlights astrocytes’
involvement in synaptic modulation, memory formation, and neural synchronization, leading to
their inclusion in mathematical brain models. Concurrently, network topology plays a critical role
inneural function, with models such as random and scale-free networks offering insights into
connectivity patterns. In this work we present the investigation of a recently published astrocyte-
neuron network model [1,2], hereafter named SGBD model, consisting of excitatory and
inhibitory
leaky-integrate and fire (LIF) neural models endowed with astrocytes, activated by synaptic
transmission and modulating
Methods
Firstly, a modified version of the model is proposed in order to overcome the limitations of the SGBD model by incorporating biologically plausible features that are more compatible with the experimental results, in particular with regard to the spatial distribution of inhibitory neurons, astrocyte dynamics such as to trigger more realistic calcium oscillations, and neuron-astrocyte connections that are more intuitively linked to their spatial positioning. Then, the role of neuron-neuron connectivity is investigated by comparing randomandhub-driven connectivitiesinboth incoming and outcoming connections.Simulations are implemented using the Brian2 simulator, allowing for a comparative analysis of neural network activity with and without astrocytes.
Results
The proposed modifications lead to a more biologically realistic representation, influencing firing rates and inter-spike interval distributions. Comparisons between random and hub-driven connectivity highlight differences in network efficiency, in particular firing activity is much larger for hub-driven connectivity even if the number of links is much lower with respect to the random connectivity. Temporal complexity of avalanches is investigated through intermittency-driven complexity tools [3,4] and significant differences are found when comparing both random vs. hub-driven and astrocyte vs. no-astrocyte.
Discussion
This study reinforces the importance of astrocytes in neural network modeling and demonstrates how connectivity patterns impact temporal complexity of firing patterns. Hub-driven degree distribution is not strictly scale-free, i.e., does not display power-law decay, but, despite this, hub-driven topology triggers the emergence of power-law behavior in the inter-spike time distributions that does not emerge in the random connectivity. Similar findings are seen in the temporal complexity of neural avalanches, where different regimes of power-law scaling behavior are found.
Acknowledgements
This work was supported by the Next-Generation-EU programme under the funding schemes
PNRR-PE-AI scheme (M4C2, investment 1.3, line on AI) FAIR “Future Artificial Intelligence
Research”, grant id PE00000013, Spoke-8: Pervasive AI.
References
[1] M. Stimberg et al. (2019), Brian 2, an intuitive and efficient neural simulator, elife8, e47314.
doi:10.7554/eLife.47314
[2] M. Stimberg et al. (2019), Modeling Neuron–Glia Interactions with the Brian 2 Simulator,
Springer, Cham, 471–505. doi:10.1007/978-3-030-00817-8_18
[3] P. Paradisi, P. Allegrini, Intermittency-driven complexity in signal processing (2017), Springer,
Cham, 161–195. doi: 10.1007/978-3-319-58709-7_6
[4] P. Paradisi et al., The emergence of self-organization in complex systems-Preface (2015),
Chaos Sol. Fract.81b, 407-411. doi: 10.1016/j.chaos.2015.09.017
