论文标题
神经元网络模型中自组织的准临界机制
Mechanisms of self-organized quasicriticality in neuronal networks models
论文作者
论文摘要
临界大脑假设指出，神经元网络有一些信息处理优势，靠近相变的关键区域。如果这是真的，我们必须询问网络如何实现和维持这种关键状态。在这里，我们回顾了几种提出的生物学机制，这些机制将关键区域变成网络参数中的动力学的吸引子，例如突触，神经元增长和触发阈值。由于神经元网络（生物学和模型）是非保守性但耗散性的，因此我们期望不是确切的关键性，而是自组织的准临界性（SOQC），该系统徘徊在临界点周围。
The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains and firing thresholds. Since neuronal networks (biological and models) are nonconservative but dissipative, we expect not exact criticality but self-organized quasicriticality (SOqC), where the system hovers around the critical point.
