From 7d2ccd2917c557e44896dd0b81746ab0b66f439e Mon Sep 17 00:00:00 2001 From: Didi Hou <didi.hou@rwth-aachen.de> Date: Wed, 20 Sep 2023 17:05:48 +0200 Subject: [PATCH] Update VERSION_LOG.md --- VERSION_LOG.md | 60 ++++++++++++++++++++++---------------------------- 1 file changed, 26 insertions(+), 34 deletions(-) diff --git a/VERSION_LOG.md b/VERSION_LOG.md index 798f5bc..4266d5e 100644 --- a/VERSION_LOG.md +++ b/VERSION_LOG.md @@ -1,49 +1,41 @@ ## MAM v1.1.0 -### New features +### New Features: -* 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/MAM2EBRAINSâ€. + * 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. ## MAM v1.0.0 -### Bug fixes -* Corrected the URL of NEST logo in README.md - +### Bug Fixes: +* Rectified incorrect NEST logo URL in README.md. -- GitLab