Available tutorials:

A simple single cell model

Level: beginner

Intro to building a morphology from a arbor.segment_tree.
Intro to region and locset expressions.
Intro to decors and cell decorations.
Building a arbor.cable_cell object.
Building a arbor.single_cell_model object.
Running a simulation and visualising the results.

A detailed single cell model

Level: advanced

Building a morphology from a arbor.segment_tree.
Building a morphology from an SWC file.
Writing and visualizing region and locset expressions.
Building a decor.
Discretising the morphology.
Setting and overriding model and cell parameters.
Running a simulation and visualising the results using a arbor.single_cell_model.

A detailed single cell recipe

Level: advanced

Building a arbor.recipe.
Building an arbor.context.
Create a arbor.simulation.
Running the simulation and visualizing the results.

A ring network

Level: advanced

Building a basic arbor.cell with a synapse site and spike generator.
Building a arbor.recipe with a network of interconnected cells.
Running the simulation and extract the results.

A simple dendrite

Level: advanced

Creating a simulation recipe of a single dendrite.
Placing probes on the morphology.
Running the simulation and extracting the results.
Investigating the influence of control volume policies.

A simple single cell recipe

Level: advanced

Building a arbor.recipe.
Using the recipe, default context and domain decomposition to create an arbor.simulation
Running the simulation and visualizing the results.

A single cell model from the Allen Brain Atlas

Level: advanced

Take a model from the Allen Brain Atlas.
Load a morphology from an swc file.
Load a parameter fit file and apply it to a arbor.decor.
Building a arbor.cable_cell representative of the cell in the model.
Building a arbor.recipe reflective of the cell stimulation in the model.
Running a simulation and visualising the results.

A single cell model from the BluePyOpt Cell Optimisation Library

Level: advanced

Export a model with optimised parameters from BluePyOpt to a mixed JSON/ACC format.
Load the morphology, label dictionary and decor from the mixed JSON/ACC format in Arbor.
Perform axon replacement with a surrogate model using the segment tree editing functionality.
Determine voltage probe locations that match BluePyOpt protocols defined with the Neuron simulator using the Arbor graphical user interface (GUI).
Create an arbor.cable_cell and an arbor.single_cell_model or arbor.recipe supporting mechanism catalogues that are consistent with BluePyOpt.
Running a simulation and visualising the results.

Distributed ring network (MPI)

Level: advanced

Building a basic MPI aware arbor.context to run a network. This requires that you have built Arbor with MPI support enabled.
Running the simulation and extracting the results.

Extracellular signals (LFPykit)

Level: advanced

Recording of transmembrane currents using arbor.cable_probe_total_current_cell
Recording of stimulus currents using arbor.cable_probe_stimulus_current_cell
Using the arbor.place_pwlin API
Map recorded transmembrane currents to extracellular potentials by deriving Arbor specific classes from LFPykit’s lfpykit.LineSourcePotential and lfpykit.CellGeometry

GPU and profiling

Level: advanced

Building a arbor.context that’ll use a GPU. This requires that you have built Arbor with GPU support enabled.
Build a arbor.domain_decomposition and provide a arbor.partition_hint.
Profile an Arbor simulation using arbor.meter_manager.

Spike Timing-dependent Plasticity Curve

Level: advanced

Two cells connected via a gap junction

Level: advanced

Creating a simulation recipe for two cells.
Placing probes.
Running the simulation and extracting the results.
Adding a gap junction connection.