Figure 6:

Figure 6:

(a) Two-photon calcium imaging performed on awake mice viewing static gratings with different spatial frequencies and orientations (180 total stimuli) using GCaMP6f at 15.5Hz. Shown are representative fluorescence traces from seven of the imaged neurons. (b) Example segment of recorded fluorescence traces (black) and leave-neuron-out predicted fluorescence traces using CILDS (orange), deconv-LDS (purple), LDS (cyan), and CIFA (brown). (c-e) Correlation between the recorded fluorescence and the leave-neuron-out predicted fluorescence for CILDS versus each of the other methods. Each point represents one neuron, where the correlation is computed for each trial (196.7 seconds long) then averaged across all 15 trials. Diagonal histograms show the paired difference in performance between CILDS and one of the other methods, as indicated. The correlation is higher for CILDS than (c) LDS (p = 5.04 × 10⁻¹⁷, n = 704 neurons, paired two-tailed t-test across the population of neurons, black asterisk indicating statistical significance), (d) deconv-LDS (p = 1.7 × 10⁻²⁷, n = 704 neurons), and (e) CIFA (p = 3.19 × 10⁻⁸¹, n = 704 neurons). Note that the histograms are zoomed-in for visual clarity, and therefore the ends of the histograms are not shown. The numbered points (black circles) correspond to the examples shown in panel b. Red points indicate a statistically significant difference per neuron between CILDS and the other method being compared using a paired two-tailed t-test across trials (p < 0.05, see Methods). Note that the threshold used for the t-test means that we might expect 5% of the neurons to appear significant even if the effect is not real. (f) Flow diagram depicting decoding of visual stimuli using low-dimensional latent variables, which are obtained by applying a dimensionality reduction method to the recorded fluorescence traces. (g) Classification accuracy of the visual stimulus based on latent variables extracted using CILDS (orange), deconv-LDS (purple), and LDS (cyan). Classification was performed using a Gaussian Naive Bayes decoder, where the number of latent variables extracted by each dimensionality reduction method was systematically varied (horizontal axis). There were 180 total gratings (with different orientations and spatial frequencies) shown during the experiment, so the chance classification accuracy is 1/180 (gray dashed). The decoding window was 250 ms, which is the duration of stimulus presentation. Black error bars around the mean indicate 95% confidence intervals (Bernoulli process).