Tutorials & E-Library

This example builds the same single cell model as A simple single cell model, except using a arbor.recipe and arbor.simulation instead of a arbor.single_cell_model.

Recipes are an important concept in Arbor. They represent the most versatile tool for building a complex network of cells. We will go though this example of a model of a single cell, before using the recipe to represent more complex networks in subsequent examples.

In this example, we will set up a single dendrite represented by a passive cable. On one end the dendrite is stimulated by a short current pulse. We will investigate how this pulse travels along the dendrite and calculate the conduction velocity.

Building and testing detailed models of individual cells, then optimizing their parameters is usually the first step in building models with multi-compartment cells. Arbor supports a single cell model workflow for this purpose, which is a good way to introduce Arbor’s cell modelling concepts and approach.

In this example, a small network of cells, arranged in a ring, will be created and the simulation distributed over multiple threads or GPUs if available.

This example builds the same single cell model as adetailed single cell model, except using an arbor.recipe and arbor.simulation instead of an arbor.single_cell_model.

This time, we’ll learn a bit more about setting up advanced features using a arbor.recipe.

We can expand on the single segment cell example to create a more complex single cell model, and go through the process in more detail.

Although our simulated neurons were given a characteristic subthalamic nucleus projection neuron morphology in tutorial part C, they still only contain the default Hodgkin and Huxley types of sodium and potassium ion selective channels. We would like to make our neurons much more electrophysiologically characteristic of subthalamic nucleus neurons. There are many channel types we would want to add, but for the tutorial we will add only one new type.

We have run a reasonable number of simulations since the first tutorial (part A). Quite often we will want to analyse or record certain simulation results and perform large numbers of simulations as fast as possible (for example in parameter searching). In this tutorial we will explore methods of getting data out of neuron to be stored or analysed in other packages. We will also consider ways of speeding up the simulations and the consequences and decision we take in doing this.

At the beginning of part A, we defined our final product to be a model of a small network of rat subthalamic nucleus projection neurons with each neuron having a particular dendritic tree morphology.

In this part, we will explore how to introduce 3D spatial information into the model (i.e. where to place in 3D space dendrites and neurons), how to define multiple neurons using templates, and how to connect neurons up using NetCon.

At the beginning of the part A, we defined our final product to be a model of a network of subthalamic nucleus projection neurons.

In this tutorial, we will explore how to create additional sections, such as dendrites, how to connect them together and finally how to display the data in a new dynamic graph - the space plot.

NEURON is an extensible nerve modelling and simulation program. It allows you to create complex nerve models by connecting multiple one-dimensional sections together to form arbitrary neuron morphologies, and allows you to insert multiple membrane properties into these sections (including channels, synapses, and ionic concentrations). The interface was designed to present the neural modeller with an intuitive environment and hide the details of the numerical methods used in the simulation.

This tutorial is divided into 5 parts (A - E) and will take you, step by step, through the process of creating a complex simulation. In part A we start with the basics: how to create a single compartment neuron model with Hodgkin-Huxley conductances, how to run the simulator and how to display the simulation results. In part B we move into the more advanced topics of building multi-compartmental neurons and using different types of graphs to display the results. In part C we will replicate neurons using templates and connect these neurons together. In part D we will add new membrane mechanisms to the simulator and incorporate them in our neurons. Finally, in part E we will look at ways of saving data from the simulations and methods for increasing simulation speed.

A brief introduction to LFPy, an open-source software for calculating different brain signals from simulated neural activity.