论文标题
自我调节的批判性：通过时间相关控制神经元附近的神经元
Self Tuned Criticality: Controlling a neuron near its bifurcation point via temporal correlations
论文作者
论文摘要
先前的工作表明，可以通过反馈控制最大化平均场波动的时间相关性，可以驯服大型神经元网络的集体活动，以保持其临界点。由于这种相关性的行为与非线性动力学系统之间的不稳定性相似，因此预计该原理应控制较低的尺寸动力学系统，从而表现出连续或不连续的分叉从固定点限制循环的连续或不连续分叉。在这里，我们提供了数值证据，表明单个神经元的动力学可以在其分叉点附近控制。该方法在两个模型中进行了测试：一个2D通用的兴奋地图和范式Fitzhugh-Nagumo神经元模型。结果表明，在这两种情况下，可以根据自相关函数的第一个系数修改控制参数来自调整到其分叉点。
Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a 2D generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.
