# Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex Source: [Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:46.062Z Authors: Qianli Liao, Tomaso Poggio Published: 2016-04-13T02:59:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()