Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference
Fig 7
HNN-SBI recovers circuit mechanism of Beta Event magnitude described in previous studies.
A: Schematic of Beta Event simulations in HNN. Beta Events are generated by a simultaneous burst of subthreshold proximal (red) and distal (green) excitatory inputs to L5 pyramidal neurons. B: Average source localized MEG Beta Event waveforms recorded from two subjects. Subject 1 (top, blue) exhibits a larger magnitude trough compared to subject 2 (bottom, orange). Simulations corresponding to a posterior predictive check (PPC) are shown in black, such that the parameters were sampled from the posterior (panel C) of each respective waveform. C: Posterior distributions conditioned on large (blue) and small (orange) magnitude Beta Events demonstrate that larger proximal variance produces a larger magnitude trough. Overlap coefficients (OVL) quantifying the separability of the marginal posterior distributions conditioned on each waveform are shown on the diagonal for the corresponding parameters. The marginal distributions for distal variance are highly overlapping, and non-overlapping for the proximal variance D: PRE heatmap of shows accurate parameter recovery (dark colors) when the ground truth parameters of
are around 5 ms2, and quickly worsen (light colors) as
increases or decreases. E: PRE heatmap of
shows accurate parameter recovery across the entire range of the prior distribution for both
and
.