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Linearization of the dynamics of recurrent neural networks (RNNs) is oftenused to study their properties. The same RNN dynamics can be written in termsof the ``activations" (the net inputs to each unit, before its pointwisenonlinearity) or in terms of the ``activities" (the output of each unit, afterits pointwise nonlinearity); the two corresponding linearizations are differentfrom each other. This brief and informal technical note describes therelationship between the two linearizations, between the left and righteigenvectors of their dynamics matrices, and shows that some context-dependenteffects are readily apparent under linearization of activity dynamics but notlinearization of activation dynamics.

Brief technical note on linearizing recurrent neural networks (RNNs) before vs after the pointwise nonlinearity