论文标题
神经启发的自适应网络中的临界漂移
Critical drift in a neuro-inspired adaptive network
论文作者
论文摘要
据推测，大脑以自组织的临界状态运行，带来多种益处，例如对输入的最佳敏感性。到目前为止，自组织的临界性通常被描述为一个一维过程，其中一个参数调整为临界值。但是，大脑中可调节参数的数量很大，因此可以预期关键状态在高维参数空间内占据高维歧管。在这里，我们表明，受稳态可塑性启发的适应规则驱动神经启发的网络在关键的歧管上漂移，该网络在无活动和持续活动之间建立了系统。在漂移期间，全局网络参数继续发生变化，而系统仍然处于关键状态。
It has been postulated that the brain operates in a self-organized critical state that brings multiple benefits, such as optimal sensitivity to input. Thus far, self-organized criticality has typically been depicted as a one-dimensional process, where one parameter is tuned to a critical value. However, the number of adjustable parameters in the brain is vast, and hence critical states can be expected to occupy a high-dimensional manifold inside a high-dimensional parameter space. Here, we show that adaptation rules inspired by homeostatic plasticity drive a neuro-inspired network to drift on a critical manifold, where the system is poised between inactivity and persistent activity. During the drift, global network parameters continue to change while the system remains at criticality.
