Effective Stimulus Propagation in Neural Circuits: Driver Node Selection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915447199236096 |
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| author | Batuev, Bulat Onuchin, Arsenii Sukhov, Sergey |
| author_facet | Batuev, Bulat Onuchin, Arsenii Sukhov, Sergey |
| contents | Precise control of signal propagation in modular neural networks represents a fundamental challenge in computational neuroscience. We establish a framework for identifying optimal control nodes that maximize stimulus transmission between weakly coupled neural populations. Using spiking stochastic block model networks, we systematically compare driver node selection strategies - including random sampling and topology-based centrality measures (degree, betweenness, closeness, eigenvector, harmonic, and percolation centrality) - to determine minimal control inputs for achieving inter-population synchronization. Targeted stimulation of just 10-20% of the most central neurons in the source population significantly enhances spiking propagation fidelity compared to random selection. This approach yields a 64-fold increase in signal transfer efficiency at critical inter-module connection densities. These findings establish a theoretical foundation for precision neuromodulation in biological neural systems and neurotechnology applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effective Stimulus Propagation in Neural Circuits: Driver Node Selection Batuev, Bulat Onuchin, Arsenii Sukhov, Sergey Neurons and Cognition Neural and Evolutionary Computing Quantitative Methods Precise control of signal propagation in modular neural networks represents a fundamental challenge in computational neuroscience. We establish a framework for identifying optimal control nodes that maximize stimulus transmission between weakly coupled neural populations. Using spiking stochastic block model networks, we systematically compare driver node selection strategies - including random sampling and topology-based centrality measures (degree, betweenness, closeness, eigenvector, harmonic, and percolation centrality) - to determine minimal control inputs for achieving inter-population synchronization. Targeted stimulation of just 10-20% of the most central neurons in the source population significantly enhances spiking propagation fidelity compared to random selection. This approach yields a 64-fold increase in signal transfer efficiency at critical inter-module connection densities. These findings establish a theoretical foundation for precision neuromodulation in biological neural systems and neurotechnology applications. |
| title | Effective Stimulus Propagation in Neural Circuits: Driver Node Selection |
| topic | Neurons and Cognition Neural and Evolutionary Computing Quantitative Methods |
| url | https://arxiv.org/abs/2506.13615 |