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Neuroscientists classify neurons into different types that perform similarcomputations at different locations in the visual field. Traditional methodsfor neural system identification do not capitalize on this separation of 'what'and 'where'. Learning deep convolutional feature spaces that are shared amongmany neurons provides an exciting path forward, but the architectural designneeds to account for data limitations: While new experimental techniques enablerecordings from thousands of neurons, experimental time is limited so that onecan sample only a small fraction of each neuron's response space. Here, we showthat a major bottleneck for fitting convolutional neural networks (CNNs) toneural data is the estimation of the individual receptive field locations, aproblem that has been scratched only at the surface thus far. We propose a CNNarchitecture with a sparse readout layer factorizing the spatial (where) andfeature (what) dimensions. Our network scales well to thousands of neurons andshort recordings and can be trained end-to-end. We evaluate this architectureon ground-truth data to explore the challenges and limitations of CNN-basedsystem identification. Moreover, we show that our network model outperformscurrent state-of-the art system identification models of mouse primary visualcortex.

Neural system identification for large populations separating "what" and "where"