Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference
Fig 5
HNN simulations that mimic RC circuit.
HNN simulations that reflect the nearly identical parameter configuration as the RC circuit in Fig 3. A: Simulated current dipole waveforms are shown for two exemplar parameter configurations with Δt = 0 (blue) and Δt ≠ 0 (orange). The original “Raw” simulated waveform (top) is plotted in comparison with the PCA inverse transformed waveform with 30 components (PCA30, bottom). B: Posterior distributions showing the inferred values that can generate the waveforms from panel A demonstrate that when the latency between the inputs is zero (blue), their amplitudes are indeterminate as visible as a dispersed distribution on panels B(a-c, blue), with a positive correlation between the parameters P and D on panel B(b, blue). Unlike the previous example (Fig 3), the indeterminacy is notably smaller for Δt = 0, with the posterior distributions primarily being concentrated around the ground truth parameters for P and D (stars on panels B(a,c)). C: Schematic of HNN simulations in which a single excitatory proximal/distal input with variable synaptic conductances and latencies produce positive (red)/negative (green) deflections in the current dipole.