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  • title: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
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            Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
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            Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

            Dec 12, 2019

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            Martin Schrimpf

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            About

            Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep and branching architectures, demonstrating increasingly better object categorization performance, yet bringing into question how brain-like they still are. In particular, typical…

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            NIPS 2019

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            About NIPS 2019

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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