Recurrent neural networks (RNNs) have yielded promising results for both
recognizing objects in challenging conditions and modeling aspects of primate
vision. However, the representational dynamics of recurrent computations remain
poorly understood, especially in large-scale visual models. Here, we studied
such dynamics in RNNs trained for object classification on MiniEcoset, a novel
subset of ecoset. We report two main insights. First, upon inference,
representations continued to evolve after correct classification, suggesting a
lack of the notion of being ``done with classification''. Second, focusing on
``readout zones'' as a way to characterize the activation trajectories, we
observe that misclassified representations exhibit activation patterns with
lower L2 norm, and are positioned more peripherally in the readout zones. Such
arrangements help the misclassified representations move into the correct zones
as time progresses. Our findings generalize to networks with lateral and
top-down connections, and include both additive and multiplicative interactions
with the bottom-up sweep. The results therefore contribute to a general
understanding of RNN dynamics in naturalistic tasks. We hope that the analysis
framework will aid future investigations of other types of RNNs, including
understanding of representational dynamics in primate vision.

通过研究大规模可视化模型中循环神经网络（RNN）的表征动力学，发现推理过程中表征持续演化，且错分表征表现出较低的 L2 范数激活模式，并更靠近读出区域的边缘，这种排列方式有助于错分表征随时间进展逐渐进入正确的区域，结果对于理解自然任务中 RNN 动力学具有普适意义。