MAM 1.2.0

Provisional description:

What's Changed

• Add correlation toolbox to enabling compute correlation coefficient and plot correlation coefficient by @didi-hou in #45
• Allow simulating only some of the areas by @didi-hou in #46
• Code, comment and displlay enhancements for the jupyter notebook by @didi-hou in #47
• Create modules for computing and loading data instead of using functions in analysis.py by @didi-hou in #48
• Plot time averaged population rates by @didi-hou in #49
• Code improvement by @didi-hou in #50
• Correct and enhance readme by @didi-hou in #51
• Improve firing rate plot by @didi-hou in #52
• Synaptic weight and k scaling by @didi-hou in #53
• Fix error computing correlation by @didi-hou in #54
• Reproducibility by @mlober in #57
• Color scheme interareal connectivity by @didi-hou in #56
• Instantaneous firing rate across all areas by @didi-hou in #58
• Parameter setting for down-scaled MAM by @didi-hou in #59
• Bug fixing, code and text improving by @didi-hou in #60
• Functional connectivity by @didi-hou in #61
• Plots enhancing by @didi-hou in #62
• Notebook documentation by @didi-hou in #63
• Test suite by @didi-hou in #66
• Remove Harden Runner by @didi-hou in #67
• Code cleaning and documentation by @didi-hou in #64
• Fix the bug due to deprecation of np.float by @didi-hou in #65
• CI test on EBRAINS by @didi-hou in #68
• Fix package python-louvain by @didi-hou in #69
• fix np.float deprecated by @didi-hou in #70
• Correct the areas order in the plot of functional connectivity by @didi-hou in #71
• Remove louvain_communities dependence in mam-notebook by @shimoura in #72
• add except ValueError in VisualCortex_Data.py by @shimoura in #73

Full Changelog: v1.1.1...v1.2.0

MAM 1.1.1

The release MAM 1.1.1 brings a number of improvements and new features, which enhance the model's reliability, usability, and overall user experience.

Check out more details in Pull requests #43

New features

• Visualizing convolved rate time series in resting state plots

Enhancements

• Added tests/.ipynb_checkpoints/* in .gitignore to ignore checkpoints
• Add modules M2E_compute_corrcoeff.py and M2E_compute_rate_time_series.py
• Module renamed: M2E_LOAD_DATA to M2E_load_data.py, M2E_visualize_instantaneous_and_mean_firing_rates.py to M2E_visualize_firing_rates.py

Bug fixes

• Fixed bug in multiarea_model/analysis.py by changing DataFrame.append to DataFrame.concat as pandas deprecated the former syntax. It's done to be compatible with EBRAINS-23.09-rc2

MAM 1.1.0

The release MAM 1.1.0 brings many improvements and new features, which enhance the model's reliability, usability, and overall user experience. It also serves as a base version specifying the dependencies required to run on EBRAINS for future releases up to and including MAM 2.0.0.

New Features:

• 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.

• 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.

• 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 by Schmidt M et al. (2018), allowing users flexible area selection, altered synchrony representation, and a focus on binned spike histograms.

Enhancements:

• 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.

Code Optimizations:

• Minimized irrelevant print statements in codebase for clearer user outputs.
• Updated .gitignore to exclude checkpoint files.

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 1.0.0

This code implements the spiking network model of macaque visual cortex developed at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich.

The model has been documented in the following publications:

1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ Multi-scale account of the network structure of macaque visual cortex Brain Structure and Function (2018), 223: 1409 https://doi.org/10.1007/s00429-017-1554-4

2. Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology, 13(2): e1005179. https://doi.org/10.1371/journal.pcbi.1005179

3. 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

The code in this repository is self-contained and allows one to reproduce the results of all three papers.

The code was improved since the above publications to function with more modern versions of NEST (3+) as well as other minor updates.