Autonomous Optimization of Targeted Stimulation of Neuronal Networks
Fig 6
A closed-loop learning session in an example network.
A closed-loop learning session in an example network. The session consisted of 1000 trials (200 training (Ti, red), 50 testing (Xi, green) trials and 4 such pairs) (A) Raster diagram showing the activity at the recording channel around the time of stimulation. Trials interrupted by ongoing activity are left empty at t > 0 s. The spikes of the interrupting SB were removed in (A) and (B) for clarity. Successful stimuli evoked responses at t > 0 s. Blue lines mark the period of latency prior to the stimulus at t = 0 s Magenta triangles indicate stimuli delivered in preceding trials. Within training rounds, the controller was free to explore the state space. Note that these rounds are in closed-loop mode but with a random sequence of stimulation latencies. The strategy in this example was non-greedy. During testing rounds the hitherto best policy was chosen. After the final round, a latency of ≈ 1.4 s was learned. Stimulus properties were as in Fig 1. (B) Zoom-in on responses evoked throughout the session. Interrupted trials appear as empty rows; in this example all stimuli elicited responses. (C) Stimulus efficacy estimated as the response strength per SB (RS/SB) computed over each of the training/testing rounds. RS/SB improved considerably during testing compared to the training rounds. The fraction of trials interrupted in each round is shown as red circles and numerically. The dashed line was added for clarity.