Design of Pinning Control Strategies of Different Neural Population Networks for Neuromodulation Research

Abstract

In order to comply with the development trend of the “Brain Project”, China has listed the neural basis for explaining cognitive function as a core pillar, emphasizing the investment of resources and research capabilities into urgent social needs such as early diagnosis and intervention treatment of neurological and psychiatric diseases. The control scheme proposed on the basis of neural computational models can predict changes in brain dynamics induced by neurostimulation, which helps to develop more effective treatment plans for neurological and psychiatric diseases, while reducing the risk of brain damage and secondary injury that may result from direct animal experiments or clinical trials. The paper presents such a theoretical method for modulating brain dynamics based on the concept of pinning control from complex network control theory to suppress spikes generated by neural population networks affected by measurement noise. The main issues to be addressed of the work include: how to select driving nodes (the locations where need to exert neurostimulation) to better ensure the effectiveness of pinning strategies for neural population networks with different topologies? What are relationships among control gain, control energy, coupling strength and the number of driving nodes while ensuring the effectiveness of control strategies? To solve these problems, firstly, based on the Wendling-type neural population model, graph theory and complex network theory are applied to construct neural population networks with “nearest-neighbor”, “scale-free” and “small-world” topologies, respectively. Then, different pinning control strategies are designed to modulate the brain dynamics simulated by the established network models according to the degree distribution, and better strategies are determined through simulation experiments. The local control adopts the output-feedback method based on the fuzzy regulator and cubature Kalman filter algorithm. Finally, the effects of coupling strength and number of driving nodes on control gain amplitude and control energy are studied using analytical and statistical methods. This work provides new ideas for the development of neuromodulation strategies in the treatment of neurological and psychiatric diseases, and is expected to play a potential role in future clinical applications.

Keywords

• Cubature Kalman filter algorithm
• fuzzy regulator
• neural population networks
• neuromodulation research
• pinning control