Estimation of neural network model parameters from local field potentials (LFPs)
Fig 11
Robustness of estimates with Gaussian spread of model parameters in test data.
A–C, estimation errors for η, g, and J, respectively, when the synaptic time delay td is randomly distributed when generating test data LFPs. td has a truncated Gaussian distribution around the fixed value td = 1.5 ms used when generating training data, and results for different values of the standard deviation σ are shown (note logarithmic scale). The Gaussian distributions are truncated at 0.2 and 2.8 ms. Results for the average estimation errors across both the full parameter space and the restricted AI parameter spaces are shown. D–F, same as A–C when the neuron membrane time constant τm instead is randomly distributed around the training-data value τm = 20 ms when generating test data LFPs. The Gaussian distributions are truncated at 2 and 38 ms. G–I, same as A–C when the neuronal firing threshold θ instead is randomly distributed around the training-data value θ = 20 mV when generating test data LFPs. The Gaussian distributions are truncated at 12 and 28 mV. J–L, same as A–C when the refractory period tref instead is randomly distributed around the training-data value tref = 2.0 ms when generating test data LFPs. The Gaussian distributions are truncated at 0.2 and 3.8 ms. M, Illustration of probability density function (pdf) of truncated Gaussian parameter distributions used in panels A–L.