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。

递归神经网络中物体识别的表示动态特性

Characterising representation dynamics in recurrent neural networks for  object recognition

The human visual system is an intricate network of brain regions that enables
us to recognize the world around us. Despite its abundant lateral and feedback
connections, object processing is commonly viewed and studied as a feedforward
process. Here, we measure and model the rapid representational dynamics across
multiple stages of the human ventral stream using time-resolved brain imaging
and deep learning. We observe substantial representational transformations
during the first 300 ms of processing within and across ventral-stream regions.
Categorical divisions emerge in sequence, cascading forward and in reverse
across regions, and Granger causality analysis suggests bidirectional
information flow between regions. Finally, recurrent deep neural network models
clearly outperform parameter-matched feedforward models in terms of their
ability to capture the multi-region cortical dynamics. Targeted virtual cooling
experiments on the recurrent deep network models further substantiate the
importance of their lateral and top-down connections. These results establish
that recurrent models are required to understand information processing in the
human ventral stream.

本研究采用时间分辨脑成像和深度学习的方法，探究人脑视觉处理的层次动态及信息流向，结果表明，回归深度神经网络模型比参数匹配前馈模型更能准确捕捉人脑视觉处理的多区域功能

需要使用复现来捕获人类视觉系统的表现动态

Recurrence is required to capture the representational dynamics of the  human visual system

Comparing different neural network representations and determining how
representations evolve over time remain challenging open questions in our
understanding of the function of neural networks. Comparing representations in
neural networks is fundamentally difficult as the structure of representations
varies greatly, even across groups of networks trained on identical tasks, and
over the course of training. Here, we develop projection weighted CCA
(Canonical Correlation Analysis) as a tool for understanding neural networks,
building off of SVCCA, a recently proposed method (Raghu et al., 2017). We
first improve the core method, showing how to differentiate between signal and
noise, and then apply this technique to compare across a group of CNNs,
demonstrating that networks which generalize converge to more similar
representations than networks which memorize, that wider networks converge to
more similar solutions than narrow networks, and that trained networks with
identical topology but different learning rates converge to distinct clusters
with diverse representations. We also investigate the representational dynamics
of RNNs, across both training and sequential timesteps, finding that RNNs
converge in a bottom-up pattern over the course of training and that the hidden
state is highly variable over the course of a sequence, even when accounting
for linear transforms. Together, these results provide new insights into the
function of CNNs and RNNs, and demonstrate the utility of using CCA to
understand representations.

作者们通过投影加权规范相关分析法（projection weighted CCA）研究神经网络的表征学习。研究发现，广泛的神经网络更容易获得相似的表征，与学习速率相关的神经网络收敛到具有不同表征的不同聚类中，RNN 随着时间的推移呈自底向上的模式聚合，而其隐藏状态在序列的不同时间间隔内变化较大。