A rotation-equivariant convolutional neural network model of primary visual cortex
This provides a new tool for studying nonlinear computations in V1, advancing neuroscience by moving beyond classical models, though it is incremental as it builds on existing CNN approaches.
The researchers tackled the problem of understanding features beyond orientation selectivity in primary visual cortex (V1) neurons by using a rotation-equivariant convolutional neural network to categorize neurons and identify common features, showing it outperforms a regular CNN and reveals non-Gabor-like features in 6000 neurons.
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that models based on convolutional neural networks (CNNs) lead to much more accurate predictions, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance. Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations. We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations. We fit this model to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging. We show that our rotation-equivariant network not only outperforms a regular CNN with the same number of feature maps, but also reveals a number of common features shared by many V1 neurons, which deviate from the typical textbook idea of V1 as a bank of Gabor filters. Our findings are a first step towards a powerful new tool to study the nonlinear computations in V1.