HuMAM 2.0.1

What's Changed

• Improve documentation notes on tutorial notebook by @shimoura in #14
• Small fix to package installation command in humam_tutorial.ipynb by @shimoura in #15

Full Changelog: v2.0.0...v2.0.1

HuMAM 2.0.0

This release includes significant updates and improvements, primarily focusing on compatibility with NEST 3.8 and updates to various dependencies.

Enhancements:

• Updated README.md with a new description of the model and compatibility notes for different versions of the NEST simulator.
• Upgraded humam.yml to use NEST simulator version 3.8 and the latest versions of other Python packages.
• Improved humam_tutorial.ipynb with checks to avoid redundant computations and enhanced plotting parameters.
• Enhanced src/analysis.py for better plotting and data handling, ensuring compatibility with newer Python versions.
• Updated src/network.py to handle output paths more robustly and ensure directory creation is recursive.
• Refactored src/simulation.py to rename spike_detector to spike_recorder, updated directory handling for spike paths, and improved neuron parameter distribution and synaptic delay handling.

Bug fixes:

• Fix the link to run the notebook on EBRAINS.

Note: This version no longer runs with NEST below 3.0. For those who need to use NEST versions below 3.0, please use version v1.2.0.

Contributed by @shimoura in PR #10, #11, #12 and #13.

Full Changelog: v1.2.0...v2.0.0

HuMAM 1.2.0

What's Changed

• Add "how to contribute" and update installation instructions on README by @shimoura in #8
• Add unit test for network scaling factors by @shimoura in #9

Full Changelog: v1.1.0...v1.2.0

HuMAM 1.1.0

This release introduces updates to improve model usability, including the addition of a downscaling factor that allows running the model on local computers and a detailed Jupyter notebook tutorial.

New Features

Features added by @shimoura in #6:

• Implemented a downscaling factor to enable users to run the tutorial on local machines before scaling up to a supercomputer.
• Added a detailed Jupyter notebook tutorial to guide users through running the model and extracting useful information. This tutorial helps users understand the workflow and experiment with the model on a smaller scale.
• Added and refactored figure scripts to adapt them to run on the notebook tutorial.
• Added "Try it on EBRAINS" button to the main README file (it still has to be tested).

Enhancements

Enhancements and corrections to humam_tutorial.ipynb suggested by @mlober and implemented by @shimoura in #7:

• Updated to clarify scaling factors, add notes on network dynamics, and improve markdown descriptions.
• Improved the description of the cc_scalingEtoE parameter and added explanations for its role in transitioning network activity.

Full Changelog: v1.0.0...v1.1.0

HuMAM 1.0.0

The code implements the multi-scale, spiking network model of human cortex developed at the Institute for Advanced Simulation (IAS-6), Research Center Jülich. This version of the model is now published in the Cerebral Cortex Journal:

• Pronold, J., Meegen, A. van, Shimoura, R. O., Vollenbröker, H., Senden, M., Hilgetag, C. C., Bakker, R., & Albada, S. J. (2024). Multi-scale spiking network model of human cerebral cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhae409

This code version generates the figures and simulations of the publication above.

HuMAM 0.2.0

What's Changed

• Run with Python3.9
• Add scripts for supplementary figures (autapse/multapse degrees; firing rates distribution) and update data paths

Full Changelog: v0.1.0...v0.2.0

HuMAM 0.1.0

Initial development release

This code implements the multi-scale, spiking network model of human cortex developed at the Institute for Advanced Simulation (IAS-6), Research Center Jülich. The model has been documented in the following preprint:

• [1] Pronold, J., Meegen, A. van, Vollenbröker, H., Shimoura, R. O., Senden, M., Hilgetag, C. C., Bakker, R., & Albada, S. J. van. (2023). Multi-Scale Spiking Network Model of Human Cerebral Cortex. BioRxiv, 2023.03.23.533968. https://doi.org/10.1101/2023.03.23.533968

The code in this repository is self-contained and allows one to reproduce the results shown in [1].