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Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference
Fig 8
Inferring local connectivity parameters from ERP waveforms.
A: Schematic of ERP simulations in HNN. Evoked activity is driven by a fixed sequence of proximal-distal-proximal exogenous inputs. SBI is used to infer the maximal conductance strength (
) of local excitatory/inhibitory connections to the proximal/distal dendrites of L5 pyramidal neurons for example waveforms. B: Exemplar simulated ERPs (blue and orange solid lines) with differing local connectivity strengths chosen from a defined prior distribution (described in the text) are shown, along with the fixed timing of the sequence of exogenous inputs for each simulation (red and green arrows). C: Spike raster plots of cell specific firing for the two ERP simulation conditions from panel B. D: Posterior distributions over local connectivity parameters alongside ground truth parameters (stars on diagonal) for conditioning observations. A strong interaction between excitatory/inhibitory distal inputs (EL2 and IL2) is visible in the lower square. Overlap coefficients (OVL) quantifying the separability of the marginal posterior distributions conditioned on each waveform are shown on the diagonal for the corresponding parameters. EL2 and IL2 exhibit a small amount of overlap with OVL values of 0.011 and 0.190 respectively. In contrast EL5 and IL5 were much more distinguishable, exhibiting OVL values of 2.28e-5 and 1.59e-13 respectively. E: Local parameter recovery error (PRE) for distal inhibition IL2 indicates errors are higher for observations generated with strong excitatory EL2 and weak inhibitory IL2 distal connections.
doi: https://doi.org/10.1371/journal.pcbi.1011108.g008