Extended Data Fig. 1: Photostimulation characterization and methods.

a, Left, GCaMP6s and densely expressed, soma-localized C1V1 in the same neurons. Right, Channelrhodopsin-2 tagged with mCherry, from a different mouse. Note that non-localized channels are prominent in the neuropil background compared with soma-localized channels. b, Photostimulation protocol schematic. Top, beam position as a function of time; samples of mirror trajectory plotted at 100 kHz. Bottom, four repeats of an identical sweep were used to photostimulate neurons. c, Photostimulation triggered average images, for a neuron (left) and control (right) site from the experiment in Fig. 1b. Arrows mark the locations of both sites. d, Cumulative density plots of photostimulated neuron responses for different lateral displacements of target location from the neuron’s centre. Same data as in Fig. 1f, but note log scale of x-axis. The 15–25-μm offset caused responses that were not present at greater distances. e, Fraction of neurons that could be photostimulated as a function of the threshold for this classification. At a threshold of 5 s.d. above shuffle, more than 96% of neurons (n = 518) could be photostimulated. Shuffle distributions were computed by bootstrap resampling of activity from trials in which the neuron was not targeted. f, Fit quality of the GP tuning model versus photostimulation magnitude. Each dot is a single targeted neuron (n = 518 neurons). Spearman correlation, c = 0.084, P = 0.055. g, Mean gratings response of a neuron versus photostimulation magnitude. Each dot is a single targeted neuron (n = 518 neurons). Spearman correlation, c = 0.11, P = 0.009. h, A convolutional neural network (CNN) was trained with human-labelled data to predict whether CNMF sources were identified as a cell body or an alternative source, including distinct neural processes, excessively blurry or out-of-plane cells, or artefactual sources (see Methods). Note that many non-soma sources exhibited similar calcium transient signals as cell body sources. Because there is no objective ground-truth for this classification, held-out datasets were hand labelled, and compared to CNN labelling. One example dataset is shown here. The large majority of sources were labelled identically, but there were borderline cases for which labels differed; many cases appear to result from either human error in labelling, owing to finite human time and inconsistencies in making borderline judgments, or an overly conservative CNN criteria for cell classification. Neither of these errors are expected to affect the results presented here.