In neurosciences, the brain processes information via the firing patterns of connected neurons operating across a spectrum of frequencies. To better understand the effects of these frequencies in the neuron dynamics, we have simulated a neuronal network of Izhikevich neurons to examine the interaction between frequency allocation and intermittent phase synchronization dynamics. As the synchronized population of neurons passes through a bifurcation, an additional frequency mode emerges, enabling a match in the mean frequency while retaining distinct most probable frequencies among neurons. Subsequently, the network intermittently transits between two patterns, one partially synchronized and the other unsynchronized. Through our analysis, we demonstrate that the frequency changes on the network lead to characteristic transition times between synchronization states. Moreover, these transitions adhere to beat frequency statistics when the neurons’ frequencies differ by multiples of a frequency gap. Finally, our results can improve the performance in predicting transitions on problems where the beat frequency strongly influences the dynamics.

中文翻译：

---

同步动态中的拍频引起的转变


在神经科学中，大脑通过在一定频率范围内运行的连接神经元的放电模式来处理信息。为了更好地理解这些频率对神经元动力学的影响，我们模拟了 Izhikevich 神经元的神经元网络，以检查频率分配和间歇相位同步动力学之间的相互作用。当同步的神经元群经过分叉时，会出现一种额外的频率模式，从而实现平均频率的匹配，同时保留神经元之间不同的最可能频率。随后，网络间歇性地在两种模式之间转换，一种部分同步，另一种不同步。通过我们的分析，我们证明网络上的频率变化会导致同步状态之间的特征转换时间。此外，当神经元的频率相差多个频率间隙时，这些转换遵循拍频统计。最后，我们的结果可以提高预测拍频强烈影响动态的问题的转换性能。