Estimation of neural network model parameters from local field potentials (LFPs)
Fig 7
Accuracy of network parameter estimation.
A, Estimation error distributions for η, g and J averaged over the entire parameter space. In the plots all parameter ranges were rescaled to the interval [0, 1] for easier comparison on the lower x-axis, the upper x-axis shows the original values. The vertical line indicates the mean of both distributions. The orange curve shows the result when using the full parameter set (η ∈ [0.8, 4], g ∈ [3.5, 8] and J ∈ [0.05, 0.4]) and the blue curve when the parameter set only contains the AI state (η ∈ [1.5, 3], g ∈ [4.5, 6] and J ∈ [0.1, 0.25]). The purple line gives the estimation error of the CNN trained for the full parameter set, but evaluated on the restricted parameter set containing the AI state only. To compare the full parameter data set and the AI-only data set, they were both scaled to the range of the full parameter set. Table 7 shows the bias and standard deviation for each of the data sets and estimated parameters. B, Cumulative error distributions, the proportion of absolute errors that fall below a given value, also with all parameters rescaled to [0, 1]. This can be understood as the fraction of the data points which are reconstructed better than a specific error. The dashed black lines indicate the 90% coverage interval.