癫痫是一种常见的神经系统疾病，特别是难治性癫痫的发病机制尚不完全清楚，极大限制了癫痫治疗效果的提升。星形胶质细胞在癫痫发作中起到关键作用，可能作为癫痫治疗的靶点。本文基于临床癫痫脑电特征，建立癫痫发作的介观尺度神经元-胶质细胞耦合动力学模型，研究癫痫发作机制以及癫痫抑制策略，主要包括：

首先，基于局灶性癫痫发作动力学模型，考虑星形胶质细胞Ca²⁺波动与神经元集群发放频率的关系，建立神经元集群-星形胶质细胞群耦合动力学模型，探讨了星形胶质细胞群对癫痫发作动力学行为的影响，并模拟刺激抑制局灶性癫痫，研究刺激类型、刺激强度和刺激间隔对抑制癫痫发作的作用，结果有助于开发合理的癫痫控制策略，为临床治疗局灶性癫痫提供理论参考依据。

其次，基于Watts-Strogatz小世界网络模型，构建了星形胶质细胞-神经元耦合网络动力学模型和癫痫发作脑网络模型。采用相位同步指标PLV量化网络的同步程度，研究耦合强度和网络结构对癫痫网络动力学行为的影响。基于临床脑电图数据，拟合了癫痫发作期和发作间期的模型参数，为开发基于同步调控的癫痫治疗策略以及制定个性化癫痫治疗策略奠定了基础。

最后，提出了两种癫痫治疗策略，并评估了模拟治疗效果。基于小世界网络模型，计算网络的度分布和同步贡献率，识别影响同步发作的关键节点和连接。建立刺激控制的癫痫发作网络模型，并提出基于目标控制的刺激调控策略，通过对少数关键节点施加定向刺激，实现对癫痫发作的有效控制。建立手术切除局部关键节点的癫痫发作网络模型，并设计四种不同治疗方案，评估不同手术切除策略对脑网络动力学行为的影响，为通过刺激高连接节点来调控癫痫网络动力学行为以及制定有效的手术治疗策略提供了理论支持。

Epilepsy is a common neurological disease, especially the pathogenesis of refractory epilepsy is not fully understood, which greatly limits the improvement of epilepsy treatment effect. Astrocytes play a key role in seizures and may serve as targets for epilepsy treatment. Based on the EEG characteristics of clinical epilepsy, this thesis establishes a mesoscale neuron- astrocytes coupling dynamics model of seizures, and studies the seizure mechanism and epilepsy suppression strategies, mainly including:

Firstly, based on the focal seizure dynamics model, considering the relationship between astrocyte Ca²⁺ fluctuation and neuronal cluster firing frequency, a neuronal cluster-astrocyte population coupling dynamics model was established, the influence of astrocyte population on seizure dynamics was discussed, and the stimulation inhibition of focal epilepsy was simulated, and the effects of stimulation type, stimulation intensity and stimulation interval on seizure inhibition were studied, and the results were helpful for the development of reasonable epilepsy control strategies. It provides a theoretical reference for the clinical treatment of focal epilepsy.

Secondly, based on the Watts-Strogatz small-world network model, a dynamic model of astrocyte-neuron coupling network and a model of epileptic brain network were constructed. The Phase Locking Value (PLV) was used to quantify the synchronization degree of the network, examining the effects of coupling strength and network structure on the dynamic behaviors of the epilepsy network. Based on clinical electroencephalogram data, model parameters during seizure and interictal periods were fitted, laying the groundwork for developing synchronization-based epilepsy treatment strategies and formulating personalized epilepsy treatment plans.

Finally, two epilepsy treatment strategies were proposed and the effects of the simulated treatment were evaluated. Based on the small-world network model, the degree distribution and synchronization contribution rate of the network were calculated, and the key nodes and connections that affect synchronization onset were identified. A seizure network model was established for stimulus control, and a target-controlled stimulation regulation strategy was proposed to achieve effective seizure control by applying directed stimulation to a few key nodes. A seizure network model was built for surgical resection of local key nodes, and four different treatment plans were designed to assess the impact of various surgical resection strategies on the dynamic behavior of the brain network, providing theoretical support for regulating the dynamic behavior of epileptic networks by stimulating highly connected nodes and formulating effective surgical treatment strategies.