RFN autoset parameters mixed

- Added: Autoset parameters for a mixed RFN.
Pcolor_2d_data ticks interpolation

- Fix: Handle None better.
Access both layers of HierarchicalRFN

- Added: Can now access both layers of a Hierarchial Random Network. Allows for a better balance between input and conjunctive units, makes more sense theoretically as well
SubmitPBS to handle feeder

- In process: adding a "cmd" parameter to the script submission, in order to use feed/feeder instead of qsub directly. Fails for some stupid PATH issue for now, should try to fix it
Fisher information improvements

- Added: changed the FI 2D N-stim completely, uses Monte Carlo sampling now. MUCH better, no memory usage, can go to any N desired. Estimates are good (for mean, low Monte carlo std dev)(for std, not the best, but that's due to the high variability of values because of the divergence)
- Fix: Some plots and calls
Fixes
Cleaning, Gaussianity estimate

- Fix: cleaned up large parts of unused code…
- Added: Try to estimate the gaussianity of the posterior. Get samples, performs a normality test, check if it passes. It's highly variable, trying multiple tests (yeah yeah super bad) shows widely varying results… The samples are not that gaussian, it seems…
Plots for paper, ExperimentDescriptors

- Added: Plots to show the posterior for several population codes and fixed datapoints.
- Added: Generators/Reloaders to check the effect of HierarchicalNetworks where all the neurons are accessible. Show how this behaves for several lower layer population codes. Conjunctive layer one show that no additional layer 2 neurons should be added. Feature layer one show a nice optimal value around 0.75 when multiple items are present (0.0 for unique item, makes sense).
- Added: Generator/Reloader to see effect of varying the activation threshold. It has a nice effect of sparsing the output patterns, and rescues the feature-layer 1 with a different activation normalisation. Threshold of ~1 seems good for around 100-200 neurons.