Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet.

Optical Character Recognition (OCR) aims to recognize text in natural images.

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Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This submission describes a novel autoencoder method, that uses unsupervised learning to configure a recurrent network to encode both the current and past states of an input. I am not a mathematician nor machine learning expert, and thus am not qualified to review the work for technical merit. However, I have extensive experience in neural network modeling, and thus appreciate both the objective and purported accomplishments: the ability to train a recurrent network to store input sequences in an efficient manner using non-supervised learning. The authors describe a mechanism that addresses the problem by breaking it into two stages -- autoencoding, and then optimization -- that are carried out over different times scales.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper deals with sampling methods based on linear rate-based neural-networks. First, it shows that symmetric weights (a common constraint in many models) significantly hurt the mixing rate. Then it shows that a (more physiological) non-normal network can have a much faster mixing rate, if the connectivity is optimized for this purpose. This works even if more biological constraints (Dale's law) are imposed.

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