Fig. 5: improv enables closed-loop optimization of peak neural responses.

a improv pipeline for optimization of neural responses during calcium imaging using a ‘Bayesian Optimization’ (BO) model actor to inform the ‘Visual Stimuli’ actor to select the next stimulus to display. b Matrix of 576 possible combinations of visual stimuli consisting of moving gratings shown to each eye individually. Stimuli are moving for 5 s and held stationary for 10 s. c Online BO actor assesses a neuron’s stimulus response to the current visual stimulus and updates its estimate of the tuning curve using a Gaussian process (GP) (f), as well as the associated uncertainty (σ). The next stimulus is selected by maximizing a priority score that balances exploration (regions of high uncertainty) and exploitation (regions of high response). Estimates and uncertainty are plotted here and in (d) using a color scale normalized to 1 for visualization. d To measure similar receptive field precision, the online BO approach typically only requires 8–20 stimulus presentations, compared to an incomplete grid search of 144 stimuli (gray denotes unsampled regions). The peak tuning identified by an offline GP fit and the empirical peak tuning from the grid search agree with the peak tuning determined online. e On average, just 15 stimuli are needed to determine peak tunings of 300 neurons in real time (N = 12 imaging sessions). f Heatmaps showing the distributions of identified peak tunings for individual neurons in the pretectum (Pt, left) or the optic tectum (OT, right). Color indicates the density of tuning curve peaks across the population. White ‘x’s mark the locations where the algorithm chose to sample. In this example, the algorithm sampled primarily near the diagonal (congruent, same direction of motion to both eyes) in the Pt but chose to sample more frequently in off-diagonal areas (different direction of motion to both eyes, e.g., converging motion) in the OT.