The Python frontend for Arbor is an interface that the vast majority of users will use to interact with Arbor. This section covers how to use the frontend with examples and detailed descriptions of features.
If you haven’t set up Arbor yet, see the Python installation guide.
The workflow for defining and running a model defined in Simulations can be performed in Python as follows:
Describe the neuron model by defining an
Partition the model over the hardware resources using
Run the model by initiating then running the
Detailed information on Arbor’s Python features can also be obtained with Python’s
help function, e.g.
Help on class proc_allocation in module arbor:
| Enumerates the computational resources on a node to be used for simulation.
- Hardware context
- Domain decomposition
- Meter manager
- Cable cells
- LIF cells
- Spike source cells
- Benchmark cells
- Single cell model