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Classical models describe primary visual cortex (V1) as a filter bank oforientation-selective linear-nonlinear (LN) or energy models, but these modelsfail to predict neural responses to natural stimuli accurately. Recent workshows that models based on convolutional neural networks (CNNs) lead to muchmore accurate predictions, but it remains unclear which features are extractedby V1 neurons beyond orientation selectivity and phase invariance. Here we worktowards systematically studying V1 computations by categorizing neurons intogroups that perform similar computations. We present a framework to identifycommon features independent of individual neurons' orientation selectivity byusing a rotation-equivariant convolutional neural network, which automaticallyextracts every feature at multiple different orientations. We fit this model toresponses of a population of 6000 neurons to natural images recorded in mouseprimary visual cortex using two-photon imaging. We show that ourrotation-equivariant network not only outperforms a regular CNN with the samenumber of feature maps, but also reveals a number of common features shared bymany V1 neurons, which deviate from the typical textbook idea of V1 as a bankof Gabor filters. Our findings are a first step towards a powerful new tool tostudy the nonlinear computations in V1.

A rotation-equivariant convolutional neural network model of primary visual cortex