Autonomous Optimization of Targeted Stimulation of Neuronal Networks
Fig 5
Dependence of the optimal latency on properties of the network’s activity dynamics.
(A) Dependence of the optimal stimulus latency t* on the A − B plane. Each plane corresponds to a different value of the time constant λ of the recovery function within the range observed experimentally (0.2 ≤ λ ≤ 1.2). (inset) Zoom-in to −2 ≤ B ≤ 6.67 to reveal the monotonic rise of t* (dots and dashed line) that corresponds to the case described in Fig 4B (λ = 1). (B) Dependence of the gain in stimulation efficacy by using t* over random stimulation latencies on the time constant λ of the recovery function. μ, A, B, and σ were set to 0.6, 20, 6.67, and 1 respectively. (C) IBI distributions for the range of values observed experimentally of the location parameter μ (0.6 ≤ μ ≤ 2) for A, B, λ, σ set to 20, 6.67, 1 and 1 respectively. (D) The family of objective functions corresponding to the IBI distributions in (C) shows the near linear relationship of the optimal latencies with μ (dots and dashed line) (A, B, λ, σ were 20, 6.67, 1 and 1 respectively; colors as in (C)). (E) Summary of the dependence of the optimal stimulus latency on the A–B–μ space for λ = 1. Each plane corresponds to a different value of the location parameter μ of the IBI distribution. (inset) Zoom-in to −2 ≤ B ≤ 6.67) to reveal the rise of t* (dots and dashed line) that corresponds to the case described in Fig 4B (λ = 1, μ = 0.6).