Li Bin, Zheng Tianyi, Otsuki Reo, Sugino Masato, Shimba Kenta, Kotani Kiyoshi
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
Front Comput Neurosci. 2025 Jun 4;19:1598138. doi: 10.3389/fncom.2025.1598138. eCollection 2025.
Neural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity, suggesting a direct link between oscillatory dynamics and cognition. However, there remains a lack of computational models that can systematically simulate and investigate this effect. To address this, we construct a low-rank spiking neural network (low-rank SNN) based on the voltage-dependent theta model to explore how structured connectivity shapes oscillatory dynamics and cognitive function. Using macroscopic model analysis, we identify different network states, ranging from stationary firing to gamma oscillation. Our model successfully reproduces phase-dependent response modulation in a Go-Nogo task, consistent with findings, providing an explanation for how neural oscillation influences task performance. Besides phase dependency, our findings suggest that gamma oscillation can enhance and prolong signal response. Compared to prior studies that applied low-rank connectivity to SNNs but remained limited to stationary or weak oscillatory regimes, our work extends to population-level synchronous activity while maintaining biological plausibility under Dale's principle. Our study offers a theoretical framework for understanding how neural oscillations emerge in structured spiking networks and provides a foundation for future experimental and computational investigations into oscillatory modulation of cognition.
神经振荡，尤其是伽马振荡，对于诸如注意力、感知和决策等认知过程至关重要。实验研究表明，伽马振荡的相位调节神经元反应选择性，这表明振荡动力学与认知之间存在直接联系。然而，仍然缺乏能够系统模拟和研究这种效应的计算模型。为了解决这个问题，我们基于电压依赖型θ模型构建了一个低秩脉冲神经网络（低秩SNN），以探索结构化连接如何塑造振荡动力学和认知功能。通过宏观模型分析，我们识别出从静止放电到伽马振荡的不同网络状态。我们的模型在Go-Nogo任务中成功再现了相位依赖的反应调制，与研究结果一致，为神经振荡如何影响任务表现提供了解释。除了相位依赖性，我们的研究结果表明伽马振荡可以增强和延长信号反应。与之前将低秩连接应用于SNN但仅限于静止或弱振荡状态的研究相比，我们的工作扩展到群体水平的同步活动，同时在戴尔原则下保持生物学合理性。我们的研究为理解神经振荡如何在结构化脉冲网络中出现提供了一个理论框架，并为未来关于认知振荡调制的实验和计算研究奠定了基础。
