Esinet

* updated to accomodate pthon 3.11.9 and mne-python 1.7.0
*

* Improved LSTM architecture

* Re-implementation of the Brent-optimization method for source scaling instead of the RMS method.

Simulations
* increased default source extent from 1-40 mm to 1-50 mm
* reduced default target_snr from 2-20 to 1-20
* changed default beta exponent from 0.5-1.5 to 0.5-3 to allow for smoother source time courses overall
* source_spread can now be mixed (region growing AND spherical expansion)
* introduced source_number_weighting:
*weight = 1/number_of_sources*
i.e., the sampling weight is the inverse of the number of sources. Example: If you desire between 1 and 5 sources, the change of having a single source is 48 %, the chance of having 5 sources is 12 %.

* Implemented the reverting of the time dimension of training data as data augmentation. This improved validation loss in our tests.

* Introducing region growing for source simulations. Region growing allows for
more realistic source simulations that do not extend spherically but along a
graph of connected surface dipoles. See esinet. Simulation.settings for
details.

* Minor changes which were forgotten.