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Adding Space to Random Networks of Spiking Neurons: A Method Based on Scaling the Network Size
Cecilia Romaro1, Jose Roberto Castilho Piqueira2, A C Roque3
1Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, 14040-901, Brazil cecilia.romaro@alumni.usp.br.
Neural Computation
|March 20, 2025
Summary
This study introduces a novel boundary solution method for spatial neural networks. This approach prevents spurious spiking behavior in models of brain connectivity.
Area of Science:
- Computational neuroscience
- Network science
Background:
- Spiking neural network (SNN) models often use random graphs lacking real brain network topology.
- Incorporating spatial properties into SNNs is challenging due to boundary effects.
- Spatial extension can introduce spurious behaviors like oscillations and unbalanced neuronal activity.
Purpose of the Study:
- To develop a method for creating spatial SNNs that accurately reflect brain network structure.
- To address and prevent spurious network behaviors arising from spatial extension and boundary conditions.
Main Methods:
- Introduced a boundary solution method for SNNs with spatial extension.
- The method is based on network size scaling techniques that preserve network statistics.
- Ensures accurate representation of neuronal connections and activity.
Main Results:
- Successfully prevented spurious spiking behavior in spatial neural networks.
- The proposed method avoids oscillations and unbalanced neuronal activity.
- Maintained first- and second-order statistics of the network.
Conclusions:
- The developed boundary solution method enables the creation of more realistic spatial SNNs.
- This technique overcomes a significant hurdle in modeling brain connectivity with spatial properties.
- Facilitates the development of biologically plausible computational models of neural systems.
