We compare several ConvNets with different depth and regularization
techniques with multi-unit macaque IT cortex recordings and assess the impact
of the same on representational similarity with the primate visual cortex. We
find that with increasing depth and validation performance, ConvNet features
are closer to cortical IT representations.

通过对多单元猴 IT 皮质的记录对比了几种具有不同深度和正则化技术的 ConvNets 的表征相似性，并评估其对灵长类视觉皮层的影响。我们发现，随着深度和验证性能的增加，ConvNet 特征越接近皮质 IT 的表征。

探究卷积神经网络和灵长类动物视觉皮层的表征相似性

Examining Representational Similarity in ConvNets and the Primate Visual  Cortex

We discuss relations between Residual Networks (ResNet), Recurrent Neural
Networks (RNNs) and the primate visual cortex. We begin with the observation
that a special type of shallow RNN is exactly equivalent to a very deep ResNet
with weight sharing among the layers. A direct implementation of such a RNN,
although having orders of magnitude fewer parameters, leads to a performance
similar to the corresponding ResNet. We propose 1) a generalization of both RNN
and ResNet architectures and 2) the conjecture that a class of moderately deep
RNNs is a biologically-plausible model of the ventral stream in visual cortex.
We demonstrate the effectiveness of the architectures by testing them on the
CIFAR-10 and ImageNet dataset.

研究深度残差网络（ResNet）、循环神经网络（RNN）和灵长类视觉皮层之间的关系。作者提出了一种将浅层 RNN 与 ResNet 进行结合以及一种基于 RNN 的新型神经网络架构，并通过对 CIFAR-10 和 ImageNet 数据集的测试证明了其有效性。