Ryan Thorpe

> @cjayb I'm looking at this with fresh eyes. I agree with you that "baseline normalization" is not really justified. Instead we need to explain to users the concept of...

I like the idea of subtracting a fitted exponential function as well. Are we cool with updating our ground truth default dpl.txt file?

My only concern with placing `truncate_negative_weights` in `simulate_dipole` is that it undoes a lot of the careful siloing we've been trying to create in the `add_XXX_drive()` API. My preference would...

I'm revitalizing this PR in conjunction with #501 in order to get hnn-core running on Binder. Is it cool if I take over @cjayb?

> kwargs are a bit hard to document is the general consensus > > but let's see what the use cases are. Remember our plotting functions return `fig` object. Then...

> If we divide the histogram counts by the bin width, we can get instantaneous firing rate: > > https://jonescompneurolab.github.io/hnn-core/stable/generated/hnn_core.viz.plot_spikes_hist.html#hnn_core.viz.plot_spikes_hist I agree with this, however, we'll need to be thoughtful...

These values are set this way to maintain consistency with our previous "ground truth" default current dipole simulation. After these values get summed with the trial index and cell's GID...

Oh yes, I had forgotten about that. We updated the `event_seed` values in [`add_erp_drives_to_jones_model()`](https://github.com/jonescompneurolab/hnn-core/blob/bab819687e40c866c7c1130e1aad3ab86124d3fe/hnn_core/network_models.py#L307); however, I'm guessing that we forgot to update them in `default.json` and `_extract_drive_specs_from_hnn_params()`.

This is addressed in #431. I'll need to update myself on the status of that PR but I think it can be pushed through rather quickly.

Turns out this is actually already being addressed in #431. Sorry @raj1701!