AI 中文总结
该研究探究突触延迟对PING regime下兴奋-抑制脉冲网络振荡相位与振幅响应的影响，发现其调节振荡频率与相干性的权衡，为理解振荡脑网络的延迟依赖性调控提供计算框架。
AI 中文摘要
突触延迟是神经元通信的基本决定因素，可深刻影响皮层振荡的产生与稳定性。尽管其在塑造网络同步性中的作用已得到充分证实，但突触延迟如何调节神经元群体对瞬态扰动的集体响应仍知之甚少。本文研究了突触延迟对电导型兴奋-抑制脉冲网络中振荡活动的相位与振幅响应的影响，该网络处于锥体-中间神经元伽马（PING） regime 中。通过系统改变突触延迟，并对兴奋群体、抑制群体或整个网络施加短暂外部扰动，我们计算了网络相位响应曲线（nPRCs）和网络振幅响应曲线（nARCs），以量化振荡时序与群体相干性的变化。增加突触延迟会减慢网络振荡，同时增强群体同步性，表明振荡频率与相干性之间存在权衡关系。兴奋扰动在不同延迟下产生相对稳健的相位响应，但振幅增强呈现出明显的延迟依赖性降低；相比之下，抑制扰动对相位重置和振幅抑制均产生显著的延迟依赖性调制；而全网络刺激则兼具兴奋与抑制响应的特征。综上，这些发现确定突触延迟是控制相位重置与振幅调制平衡的关键参数，并为理解振荡脑网络的延迟依赖性调控提供了计算框架。
英文摘要
Synaptic delays are fundamental determinants of neuronal communication and can profoundly influence the emergence and stability of cortical oscillations. Although their role in shaping network synchronization is well established, how synaptic delays regulate the collective response of neuronal populations to transient perturbations remains poorly understood. Here, we investigate the effects of synaptic delays on the phase and amplitude responses of oscillatory activity in a conductance-based excitatory-inhibitory spiking network operating in the pyramidal-interneuron gamma (PING) regime. By systematically varying the synaptic delay and applying brief external perturbations to the excitatory population, inhibitory population, or the entire network, we computed network phase response curves (nPRCs) and network amplitude response curves (nARCs) to quantify changes in oscillation timing and population coherence. Increasing synaptic delay slowed network oscillations while enhancing population synchrony, demonstrating a trade-off between oscillation frequency and coherence. Excitatory perturbations produced relatively robust phase responses across delays but exhibited a pronounced delay-dependent reduction in amplitude enhancement. In contrast, inhibitory perturbations generated substantially stronger delay-dependent modulation of both phase resetting and amplitude suppression, whereas whole-network stimulation combined features of both excitatory and inhibitory responses. Taken toghether, these findings identify synaptic delay as a key parameter governing the balance between phase resetting and amplitude modulation and provide a computational framework for understanding delay-dependent control of oscillatory brain networks.
