ngpu_microcircuit

Cortical microcircuit model for NEST GPU

Taken from https://github.com/nest/nest-gpu/tree/main/python/Potjans_2014
Time of writing: 11.03.2023, last update to model: 13.02.2023

Prerequisites

These scripts were tested with NEST GPU version tag offboard and version tag onboard. Installation instructions can be found at:

• https://nest-gpu.readthedocs.io/en/latest/installation/index.html

Additionally to run the scripts to post process the data, Python and additional packages are required. To run the data post processing scripts we used:

• Python 3.8.6
• Numpy 1.22

Contents

Original files

These files did not change with respect to the source at time of writing:

• run_microcircuit.py
• helpers.py
• sim_params.py
• network_params.py
• stimulus_params.py

Modified files

These files were modified for benchmarking purposes:

• network.py:
  • Disabled Prepare and Cleanup call from connect function L97: To properly measure calibration time, Prepare and Cleanup functions were commented: L125 and L126.
  • Added get_network_size function to get total number of neurons: L200.

Additional files

These files were added for benchmarking purposes:

• run_benchmark.py: Python script based on the original simulation script of the model with additional adaptations for benchmarking, notably the addition of command line argument handling, simulation timers (cf Models sub-section in 2.4), and data exporting to json files.
  • Added handling of different Nested Loop algorithms passed as argument.
• gather_data.py: Python script designed to collect the data from all of the simulation runs of a benchmark and compute the mean values and the standard deviation of the simulation timers.
• benchmark.sh: Bash script to automatically benchmark the model with 10 different random generation seeds and collect the data.
  • By default, nested loop algorithm used is BlockStep, this can be changed in L28
• run_recording.py: Python script based on the original simulation script of the model with additional adaptations for multiple recording sessions, notably the addition of command line argument handling, simulation timers (cf Models sub-section in 2.4), and data exporting to json files.
  • This Python script is meant to be run by the recording.sh script found in this directory.
    • To run it individually, examples of local execution and SLURM execution are found in recording.sh.
  • In this script:
    • Spike recording is enabled.
    • Poisson generators are enabled.
    • Optimized membrane potentials are enabled.
    • Presimulation runs for 500ms.
    • Simulation runs for 10s.
• recording.sh: Bash script to automatically run the model while recording spikes using 10 different random generation seeds and collect the data.
  • Can be used to run locally or through SLURM with an interactive session.
    • By default, it is set to run locally. To change this, uncomment respective line execution line in script. More information can be found in the READMEs of each model directory.
    • SLURM executions assume a system equipped with 128, this can be changed through cores variable at the beginning of each script.

Execution

To run a 10 simulation benchmark using 10 different random generation seeds:

bash benchmark.sh

By default this script runs on local machines, this can be changed to run with SLURM in an interactive session by commenting L63 and uncommenting L66.

To run 10 simulations with recordings using 10 different random generation seeds:

bash recording.sh

By default this script runs on local machines, this can be changed to run with SLURM in an interactive session by commenting L63 and uncommenting L66.