To start using the framework, the user has to define a few environment variables
in a new file called `config.py`. The file `config_template.py` lists the required
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@@ -100,7 +100,7 @@ Note that it can sometimes be necessary to execute `snakemake --touch` to avoid
The files `run_example_downscaled.py` and `run_example_fullscale.py` provide examples. A simple simulation can be run in the following way:
1. Define custom parameters.
1. Define custom parameters.
See `multi_area_model/default_params.py` for a full list of parameters. All parameters can be customized.
2. Instantiate the model class together with a simulation class instance.
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@@ -127,6 +127,29 @@ The procedure is similar to a simple simulation:
Be aware that, depending on the chosen parameters and initial conditions, the network can enter a high-activity state, which slows down the simulation drastically and can cost a significant amount of computing resources.
## Extracting connectivity & neuron numbers
First, the model class has to be instantiated:
1. Define custom parameters.
See `multi_area_model/default_params.py` for a full list of parameters. All parameters can be customized.
2. Instantiate the model class.
from multiarea_model import MultiAreaModel
M = MultiAreaModel(custom_params)
The connectivity and neuron numbers are stored in the attributes of the model class.
Neuron numbers are stored in `M.N` as a dictionary (or in `M.N_vec` as an array),
indegrees in `M.K` as a dictionary (or in `M.K_matrix` as an array). To extract e.g.
the neuron numbers into a yaml file execute
import yaml
with open('neuron_numbers.yaml', 'w') as f:
yaml.dump(M.N, f, default_flow_style=False)
Alternatively, you can have a look at the data with `print(M.N)`.
## Simulation modes
The multi-area model can be run in different modes.
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@@ -236,5 +259,5 @@ Grants SFB936/A1,Z1 and TRR169/A2) and computing time granted by the
JARA-HPC Ver- gabegremium and provided on the JARA-HPC Partition part
of the supercomputer JUQUEEN (Jülich Supercomputing Centre 2015) at
Forschungszentrum Jülich (VSR Computation Time Grant JINB33), and Priority
Program 2041 (SPP 2041) "Computational Connectomics" of the German Research
Program 2041 (SPP 2041) "Computational Connectomics" of the German Research
