2 merge requests!38created VERSION_LOG.md for recording changes made for every release,!39Created VERSION_LOG.md to record all changes made in every release version
* Improved documentation: added in the README.md file the Try It On EBRAINS button and clear and detailed User instruction for users to be able to follow step-by-step instructions without much background knowledge or experience, delete section Testing on EBRAINS
* In down-scale multi-area mode, separated all external parameters to Parameters to tun and Default parameters. Parameters to tune consist of 4 parameters we decided to expose to users initially, and default parameters will be tuned by us and are not recommended for users to change
* Added section Extract and visualize interareal connectivity which plots the area-level relative connectivity as heatmaps. Two heatmaps represent the interareal connectivity of full-scale multi-area model (left) and down-scale multi-area model (right). There are small differences between them although we’re calculating relative connectivity as there’s randomness
* Added section Simulation Results Visualization. The code is written in separate modules saved as .py files in “./figures/MAM2EBRAINS” to avoid displaying contents that are not relevant to users
* Documentation Enhancements:
* Streamlined README.md with a Try It On EBRAINS button and step-by-step user instructions.
* Removed "Testing on EBRAINS" section for clarity.
* Added 3 plots in the section Simulation Results Visualization
* 3.1. Instantaneous and mean firing rate across all populations (existed in MAM v1.0.0, refined in MAM v1.1.0)
* 3.2 Resting state plots
* 3.3 Time-averaged population rates
* Parameter Tuning Improvements:
* Segregated parameters in down-scale multi-area mode into Parameters to Tune and Default Parameters.
* Introduced exposure of four user-friendly parameters, while retaining others for internal tuning.
* The 3.2 Resting state plots figure is plotted based on Fig 5. of the paper Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. https://doi.org/10.1371/journal.pcbi.1006359, yet there are a few differences:
* This plot provides the option for users to choose 3 areas to plot the raster plots instead of fixing V1, V2, and FEF to plot
* The subplot E Correlation coefficient is replaced as Synchrony
* The subplot G only plots the binned spike histograms (gray), not the convolved histograms (black)
* Visualization Augmentations:
* Introduced Extract and Visualize Interareal Connectivity to display area-level relative connectivity via heatmaps.
* Added Simulation Results Visualization section with separate code modules in “./figures/MAM2EBRAINS”.
* Enriched visualization with three new plots detailing instantaneous firing rate, resting state, and time-averaged population rates.
* Refined representation of resting state plots inspired from Schmidt M et al. (2018), allowing users flexible area selection, altered synchrony representation, and a focus on binned spike histograms.
### Enhancements
### Enhancements:
* Reconstructed the Jupyter Notebook and added Notebook structure as table of contents that enables users to navigate quickly and easily between different sections. (see the notebook structure for details)
* Notebook Refinements:
* Overhauled Jupyter Notebook structure with an accessible table of contents for user navigation.
* Enhanced parameter descriptions for both exposed and default sets.
* Incorporated model overview and concise description of the down-scaled multi-area model.
* Cross-referenced relevant publication figures for user benefit.
* Added detailed and easy-to-understand descriptions to the 4 exposed parameters and also brief comments for the default parameters
* Added the model overview diagram and a short description of the down-scaled multi-area model at the beginning of the jupyter notebook
* Added descriptions of comparable figures in our publications whenever available so that users can compare the down-scaled model with their costumed parameters and the full-scaled model presented in the paper
### Code Optimizations:
* Removed unnecessary print statements in ./multiarea_model/analysis.py and ./multiarea_model/analysis_helpers.py to avoid multiple print that are not relevant to users
* Minimized irrelevant print statements in codebase for clearer user outputs.
* Updated .gitignore to exclude checkpoint files.
* Updated ./.gitignore file to ignore checkpoint files
### Bug fixes
* Corrected the separator from "" to "/" in ./multiarea_model/data_multiarea/SLN_logdensities.R to fix the file path of ./multiarea_model/data_multiarea/bbAlt.R
* Fixed bugs in ./multiarea-model/analysis.py: change np.nan*np.ones(params['t_max'] - params['t_min']) to np.nan*np.ones(int(params['t_max'] - params['t_min']))
### Bug Fixes:
* Resolved file path separator issue in ./multiarea_model/data_multiarea/SLN_logdensities.R.
* Addressed datatype concerns in ./multiarea-model/analysis.py for array initialization.
