Comparison of open-loop predictions with autonomously learned strategies.

<p>(A) Dependence of response strengths on pre-stimulus inactivities in data during a closed-loop session in an example network. Each box shows the statistics of response strengths recorded at one discrete state. The central measures are median and the edges with 25^th and 75^th percentiles. Whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually. The fit (red) was made to the medians. The minimal latency for burst termination was 0.4 s in this example, which was thus the earliest state available for stimulation. (B) Across networks, closed-loop estimates of the gain <i>A</i> correlated strongly with open-loop estimates (r = 0.91, p<10^-5, n = 15 networks), indicating that <i>A</i> was mostly stable during the experiments. (C) Similarly, closed-loop estimates of <i>B</i> were in agreement with open-loop ones (r = 0.66, p = 0.003, n = 18 networks), although to a lesser degree. (D) Across networks, learned stimulus latencies show a positive correlation with predicted optimal values (r = 0.94, p<10^-8, n = 17 networks). (E) In spite of some variability in Panels B-D the magnitudes of the modeled objective functions for predicted and learned latencies matched closely (green dots), indicating that the network/stimulator system was performing at a near optimal regime, regardless of slight discrepancies in the latencies. Exact optima were likely unreachable owing to the coarse discretization (0.5 s) of states. Red dots denote the corresponding magnitudes at <i>t</i>_<i>rand</i> for a strategy delivering stimuli at random latencies estimated as the mean of the objective function. (F) The distribution of errors between learned and predicted latencies is centered around the predicted optimum and confined to within 2 discrete steps from it.