 layer, is minimized with respect to the input. The inverse happens in <em>gradient ascent</em>: the loss is maximized so that the input image increasingly excites the layers <span class="citation" data-cites="DeepDreamTutorial">(DeepDream)</span>, arriving at <em>activation maximization</em>. The ‘gradients’ are referred to here as the gradient vector between a loss and its inputs in a model. We have found similarities in our sonifications of the gradients and in Herrmann’s sonification of the activa