论文标题
训练精确的压力模式
Training precise stress patterns
论文作者
论文摘要
我们介绍了一个培训规则，该规则使一个由弹簧和仪表板组成的网络能够学习精确的压力模式。我们的目标是控制随机选择的“目标”键的张力。该系统是通过向目标债券应用压力来训练的，从而导致其余的债券（充当自由度的学习程度）进化。选择目标债券的不同标准会影响是否存在挫败感。当每个节点最多有一个目标键时，错误会收敛到计算机精度。单个节点上的其他目标可能导致收敛缓慢和失败。但是，即使接近麦克斯韦·卡拉丁定理预测的极限，培训也是成功的。我们通过考虑带有屈服压力的仪表板来证明这些思想的普遍性。我们表明，训练会收敛，尽管训练的损失较慢。此外，带有压力的仪表盘可阻止训练后系统放松，从而编码永久记忆。
We introduce a training rule that enables a network composed of springs and dashpots to learn precise stress patterns. Our goal is to control the tensions on a fraction of "target" bonds, which are chosen randomly. The system is trained by applying stresses to the target bonds, causing the remaining bonds, which act as the learning degrees of freedom, to evolve. Different criteria for selecting the target bonds affects whether frustration is present. When there is at most a single target bond per node the error converges to computer precision. Additional targets on a single node may lead to slow convergence and failure. Nonetheless, training is successful even when approaching the limit predicted by the Maxwell Calladine theorem. We demonstrate the generality of these ideas by considering dashpots with yield stresses. We show that training converges, albeit with a slower, power-law decay of the error. Furthermore, dashpots with yielding stresses prevent the system from relaxing after training, enabling to encode permanent memories.
