Tutorials & E-Library

In this tutorial, we will build a classic Brunel network using LIF cells in Arbor, and you can compare it with the simulation using LIF cells in the NEST simulator.

In this example, we will set up two cells connected via a gap junction. The cells have different leak potentials. We will investigate how the equilibrium potentials of the two cells change because of the gap junction connection.

In this tutorial we’ll see how we can export a model with optimised cell parameters from the BluePyOpt package. In the process, we will learn to edit the morphology in order to replace the axon with a surrogate model and use the Arbor GUI to define stimuli and probe locations that are consistent with BluePyOpt protocols for the Neuron simulator.

In this tutorial we’ll see how we can take a model from the Allen Brain Atlas and run it with Arbor.

Here we will show you how to re-create the single segment cell example in the previous tutorial in the Arbor GUI.

Grouped loosely by primary (but not exclusive!) focus, we have a set of tutorials to help you learn by doing. You can find some examples of full Arbor simulations in the python/examples directory of the Arbor repository. The examples use pandas, seaborn and LFPykit for analysis and plotting which are expected to be installed independently from Arbor. In an interactive Python interpreter, you can use help() on any class or function to get its documentation. (Try, help(arbor.simulation), for example).

The time series of a chemical concentration necessarily depends on its initial conditions; i.e. the concentration at time 0. An analogous statement is true for gating variables, etc. How do we specify this?

Option 1: NEURON and NMODL defaults
Option 2: Uniform initial concentration
Option 3: Initializing to a function of position
Option 4: to steady state

All options will be covered in this tutorial.

Radial diffusion can be implemented in rxd using multicompartment reactions. By creating a series of shells and borders with reactions between them dependent the diffusion coefficient.

Here we develop a NEURON implementation of the Leloup-Goldbeter model for circadian rhythms in Drosophila, using the version of the model specified in:

Leloup, J. C., Gonze, D., & Goldbeter, A. (1999). Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora. Journal of biological rhythms, 14(6), 433-448. https://doi.org/10.1177/074873099129000948

We'll use the initial conditions from the BioModels version: https://www.ebi.ac.uk/biomodels/BIOMD0000000298

We have expanded the capabilities the NEURON reaction diffusion module to support a macroscopic model of the extracellular space, described in a recent paper. Here is brief a tutorial that provides an overview of the python interface.

NEURON's reaction-diffusion infrastructure can be used to readily allow intracellular concentrations to respond to currents generated in MOD files, as long as:

nrn_region='i' is specified for the rxd.Region (so that it knows it corresponds to the electrophysiology region of the inside of the cell), AND the name and charge of the ion/etc are given in the rxd.Species declaration.

Satisfying the above two rules also allows MOD files to see intracellular concentrations.

3D extracellular concentrations also interoperate with electrophysiology automatically as long as name and charge are specified.