论文标题
在非平衡图中的狂热转向
Frenetic steering in a nonequilibrium graph
论文作者
论文摘要
在神经网络的传统识别任务中，潜在的景观或成本功能可指导系统使用梯度动力学的模式。这不是大脑的作用方式，因为其动力学远非平衡。我们提供了在非平衡模型中进行模式恢复的替代性和原理证明，仅改变时间对称动力学。作为数学模型，在随机取向完整图上的随机步行者可能会朝着弧方向驱动有限驱动。图形的某些顶点表示模式。第一个算法构建了这些模式的吸引力盆地。第二种算法更新了过渡速率中的时间对称因素，以便沃克（Walker）快速达到模式并在其吸引盆地的顶点开始时保持足够长的时间。
In traditional recognition tasks of neural networks, a potential landscape or cost function guides the system toward patterns using gradient dynamics. That is not how the brain works as its dynamics is far from equilibrium. We present an alternative and proof of principle for pattern recovery in a nonequilibrium model whereby only time-symmetric kinetics are altered. As a mathematical model, a random walker on a randomly-oriented complete graph is subject to finite driving in the direction of the arcs. Some vertices of the graph represent patterns. A first algorithm constructs basins of attraction for these patterns. A second algorithm updates the time-symmetric factors in the transition rates, in order for the walker to quickly reach a pattern and remain there for a sufficiently long time, whenever starting from a vertex in its basin of attraction.
