Estimating receptive fields in large neural populations is often limited by experimental time: stimuli with theoretically favorable statistics for receptive-field estimation may, in practice, evoke too few informative spikes in neurons with high specificity for structured features, requiring impracticably long recordings. Therefore, we asked whether a structured stimulus could broadly engage neural populations across stages of the early visual system while permitting a tractable and interpretable reverse-correlation analysis. To address this question, we developed "reverse correlation against stimulus elements (RCASE)", an analysis framework for stimuli that can be represented frame by frame through the presence or absence of specific stimulus elements. We then designed such a stimulus consisting of random moving objects (RMO) and compared RMO/RCASE with conventional spike-triggered averaging using a dense binary white noise (WN/STA) stimulus across different stages of the early visual system of the mouse, namely the retina, the nucleus of the optic tract, the superior colliculus and the primary visual cortex, and in the primate retina. To distinguish the contributions of spatial and temporal scales, motion, stimulus structure, and analysis choice, we additionally compared against WN/STA with different square sizes and frame rates, spike-triggered covariance analysis, as well as random static objects and spatially correlated cloud stimuli. RMO evoked stronger and more reliable responses than WN in the mouse retina and across the investigated mouse visual areas. RMO/RCASE yielded discernible receptive fields for a substantially larger proportion of neurons and in a fraction of the recording time. Although the stimulus objects featured localized moving contrast edges, this advantage was not restricted to direction-selective cell populations. In the primate retina, in contrast, WN/STA approached the performance of RMO/RCASE. RMO/RCASE also supported simultaneous estimation of stimulus direction, speed, and color tuning. Our approach enables rapid and interpretable functional characterization of large populations of visual neurons, enabling to simultaneously recover receptive fields and feature tuning, whenever a low-dimensional feature space can be specified beforehand.
Buettner, M., Znidaric, M., Diggelmann, R., Rosselli, F. B., Bucci, A., Hierlemann, A., Franke, F.
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