P230 Driver Nodes for Efficient Activity Propagation Between Clusters in Spiking Neural Networks
Bulat Batuev1+,Arsenii Onuchin2,3+, Sergey Sukhov1
1Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Moscow, Russia
2Skolkovo Institute of Science and Technology, Moscow, Russia
3Laboratory of Complex Networks, Center for Neurophysics and Neuromorphic Technologies, Moscow, Russia
+ These authors contributed equally
Email: arseniyonuchin04.09.97@gmail.com
Introduction
Synchronous neural activity is critical for brain function, yet the connectome's role in enabling synchronization remains unclear. We explore strategies to achieve widespread synchronization in spiking stochastic block model (SBM) networks with minimal control inputs. This work builds on research into neural network control [1], focusing on identifying driver nodes that influence dynamics. By evaluating centrality measures (betweenness, degree, eigenvector, closeness, harmonic, percolation), we pinpoint topological features predicting effective driver nodes. Furthermore, we analyze connectivity patterns to understand pairwise activity relationships and uncover mechanisms of network-wide coordination.
Methods
The spiking neural network consisted of 500 leaky integrate-and-fire neurons (80% excitatory, 20% inhibitory) divided into two clusters of equal size, with intra-cluster edge probability 0.15 and inter-cluster varying from 0.06 to 0.13. To simulate background activity, all neurons received independent Poisson-distributed inputs. Within the first cluster, a subpopulation of neurons (10–20%) was designated as driver neurons and subjected to an additional external current stimulus (10 Hz, 1000 pA). Driver neurons were selected either randomly or by centrality metrics (betweenness, degree, eigenvector, closeness, harmonic, percolation) [2]. Neural dynamics were simulated for 5 seconds to achieve steady-state activity using the Brian 2 [3].
Results
The population activity in the non-stimulated cluster was analyzed as a function of the number of driver neurons and the inter-cluster connectivity. When driver neurons were selected using closeness and betweenness centrality metrics, spike rates in the second cluster increased approximately 10-fold compared to random selection, accompanied by synchronization with the first cluster at nearly 10 Hz. In contrast, selecting driver neurons based on degree and percolation centrality metrics resulted only in 5-fold increase compared to random selection (Fig. 1).
Discussion
Synchronization between two weakly coupled clusters can be achieved by selectively stimulating specific neurons within the first cluster. However, it remains unclear why closeness and betweenness centrality outperform other centralities in promoting synchronization. Future research could focus on extending our method to multicluster heterogeneous systems. While the two-cluster model offers a controlled setting, expanding it could provide deeper insights into real brain connectomes. In conclusion, this study elucidates how topology and driver node selection shape neural synchronization, with potential applications in neuromodulation and brain-inspired systems.
Figure 1. The average population activity within the second cluster, calculated over a 1-second time window, is depicted for driver nodes selected based on various centrality measures (degree, betweenness, eigenvector centrality, PageRank, and percolation centrality) for the upper surface, and for nodes chosen at random for the lower surface.
Acknowledgements
This work was funded by the Russian Science Foundation (project number 24-21-00470).
References
1.Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S. (2015). Self-organization of synchronous activity propagation in neuronal networks driven by local excitation.Frontiers in Computational Neuroscience, 9, 69. https://doi.org/10.3389/fncom.2015.00069
2.Saxena, A., & Iyengar, S. (2020). Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190.
3.Stimberg, M., Brette, R., & Goodman, D. F. (2019). Brian 2, an intuitive and efficient neural simulator.eLife, 8, e47314. https://doi.org/10.7554/eLife.47314
