Linearization of the dynamics of recurrent neural networks (RNNs) is often used to study their properties. The same RNN dynamics can be written in terms of the ``activations" (the net inputs to each unit, before its pointwise nonlinearity) or in terms of the ``activities" (the output of each unit, after its pointwise nonlinearity); the two corresponding linearizations are different from each other. This brief and informal technical note describes the relationship between the two linearizations, between the left and right eigenvectors of their dynamics matrices, and shows that some context-dependent effects are readily apparent under linearization of activity dynamics but not linearization of activation dynamics.

翻译：循环神经网络动力学的线性化常用于研究其特性。同一RNN动力学可以用“激活值”（每个单元在逐点非线性之前的净输入）或“活动值”（每个单元在逐点非线性之后的输出）表示，这两种对应的线性化方式互不相同。本简短非正式技术说明阐述了两类线性化之间的关系、其动力学矩阵左右特征向量的关联，并表明某些情境依赖效应在活动动力学线性化中易于显现，但在激活动力学线性化中则不然。