New preprint on understanding V1 computation using rotation-equivariant neural networks

meis.pngI developed an approach to organize and classify neurons in V1 according to their nonlinear computation, ignoring receptive field location and preferred orientation. We use a rotation-equivariant convolutional network to perform weight sharing not only across space, but also across orientation. Our preprint describes the approach and some early results we obtained using recordings of around 6000 neurons in mouse V1.

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