Autonomous Optimization of Targeted Stimulation of Neuronal Networks
Fig 8
Performance evaluation of the controller.
(A) The percentage of interrupted trials during training (x-axis) and testing (y-axis) sessions (n = 52 pairs across 11 networks). This percentage decreased sharply after learning in 94.2% of the recorded sessions. (B) The mean RS evoked per stimulus was, however, preserved in both sessions. (C) The variability in RS per stimulus decreased significantly (p = 0.01, two-sample t-test). (D) Comparison of the optimal stimulus efficacies predicted from our models with the efficacies achieved during the final closed-loop testing sessions. Vertical bars represent 99% confidence intervals corresponding to the models fitted for each network. Achieved values fall within the interval in 8/11 networks studied. (E) Mean rewards were calculated over trials in the final training and testing rounds to compare the controller’s performance. After learning, mean rewards increased in each network, which is indicative of the improvement in stimulation efficacy. The rewards across the sequence of trials in each round were drawn from distinct distributions in every network (p<0.002, two-sample Kolmogorov-Smirnov test). The individual distributions are shown in S2 and S3 Figs. (F) Summary of learning across networks on a normalized RS/stimulus vs. interruption probability plane (11 networks). Only final training and testing rounds were considered. Normalization for interruptions was performed relative to the model-based estimate of interruption probabilities, corresponding to stimulation at random latencies for each network. The RS/stimulus measure was similarly normalized to the model-based estimates of the efficacy assuming a random stimulation strategy. The improvement in performance clearly separates the data points in the plane. Of the two modalities that contribute to stimulus efficacy, the improvement was dominated by reduction of interruption probabilities.