[MRG] ENH: Advanced Features and Optimization in `BatchSimulate`

[samadpls]

A follow-up PR to extend and refine the functionality of BatchSimulate.

[ntolley]

This is coming along great @samadpls! I think now is a good time to increase the robustness of the BatchSimulate class by adding more extensive type/option checks for the parameters passed into the __init__() and methods

You can use _validate_type() and _check_option() to do this: https://github.com/jonescompneurolab/hnn-core/blob/b2a211af18924070c64ab3cb3c6c1684ad53d3d4/hnn_core/externals/mne.py#L175

For each check you'll want to write a unit test to make sure an error is raised when you attempt to pass an invalid value. You can check out the unit tests elsewhere to see how this is done:

https://github.com/jonescompneurolab/hnn-core/blob/b2a211af18924070c64ab3cb3c6c1684ad53d3d4/hnn_core/tests/test_network.py#L74

[ntolley]

For the next PR here are the goals

• Add options to specify which simulation results should be saved (dipole, spiking, LFP, voltages, currents, and calcium)
• Add ability to save parameter values alongside simulation results
• Add plot to example the shows dipole waveforms overlaid
• Update x-axis on example to show the parameter value being varied (i.e. "Synaptic Strength (nS)")

To-do for me: figure out why linkcheck fails when BatchSimulate is added to the hnn_core __init__