Autonomous Optimization of Targeted Stimulation of Neuronal Networks
Fig 3
Identification of network specific objective functions.
(A) Networks of dissociated neurons in vitro exhibit activity characterized by intermittent network-wide spontaneous bursts (SB) separated by periods of reduced activity (raster plot for 60 channels in a DIV 27 network). The shading marks the limits of individual SBs as detected by the burst-detection algorithm. (B) The distribution of Inter-Burst Intervals (IBIs) is approximately lognormal. The histogram shows the IBI distribution for the network in (A). The cumulative of this distribution (red) is predictive of the probability of being interrupted by ongoing activity given the elapsed period of inactivity, i.e. the current state st. (C) Such a distribution was used to weight response strengths so that each dot represents the mean response strengths that can be evoked over a set of trials, including those that did not lead to stimulation, for a given stimulation latency. The fit predicts the objective function of the optimization problem. The example shows the data for the network shown in Fig 1C. The curve reveals a quasiconcave dependency, a unique global maximum and an optimal latency of ≈ 2.5 s in this network. (D) Fits to the probability of avoiding an interruption (blue), response strengths prediction (orange), and the resulting weighted response curve (orange, dotted) shown for another network. An optimal latency of ≈ 1.5 s emerges in this case. (E) All predicted objective functions for each of the 20 networks studied were quasiconcave and unique choices of optimal stimulus latencies were available. The objective functions were normalized to peak magnitude.