School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China.
Chaos. 2023 Feb;33(2):023119. doi: 10.1063/5.0130075.
The theory of self-organized bistability (SOB) is the counterpart of self-organized criticality for systems tuning themselves to the edge of bistability of a discontinuous phase transition, rather than to the critical point of a continuous one. As far as we are concerned, there are currently few neural network models that display SOB or rather its non-conservative version, self-organized collective oscillations (SOCO). We show that by slightly modifying the firing function, a stochastic excitatory/inhibitory network model can display SOCO behaviors, thus providing some insights into how SOCO behaviors can be generated in neural network models.
自组织双稳性(SOB)理论是自组织临界性的对应物,适用于将系统调整到不连续相变双稳性的边缘,而不是连续相变的临界点。就我们所知,目前很少有神经网络模型显示 SOB 或更确切地说是其非保守版本,自组织集体振荡(SOCO)。我们表明,通过稍微修改点火函数,随机兴奋/抑制网络模型可以显示 SOCO 行为,从而为神经网络模型中如何产生 SOCO 行为提供了一些见解。