Tutorials & E-Library

In this tutorial, we create a neuron model with a “nonlinear” or “active” dendritic compartment, that can, independently from the soma, generate a dendritic action potential. Instead of modeling the membrane potential of the dendritic compartment explicitly, the dendritic action potential (dAP) is modeled here as the injection of a rectangular (pulse shaped) dendritic current into the soma, parameterized by an amplitude and a duration. The rectangular shape can be interpreted as the approximation of an NMDA spike (Antic et al. 2010). A dendritic action potential is triggered when the total synaptic current exceeds a threshold.

If you have followed the tutorial "How to use NMODL to extend Arbor’s repertoire of Ion Channels" closely, then there isn’t much more to know before you can write your own synapse models. Thus, we will just show and discuss the exponential synapse coming with Arbor.

NMODL is Arbor’s way of expressing ion channel dynamics, which in turn can be added to cable cells via the Cable cell decoration interface. This tutorial will guide you through to create such an ion channel from scratch. Note that there are already large selections of channels built into Arbor in addition to online databases of ready-to-use models. Please check there first and only if you find nothing to use or adapt, try to build your own. If you want to learn how to use ion channels, consider other tutorials first, such as A simple single-cell recipe and the dynamic catalogue example.

Save the following to a file called initn.hoc, then use NEURON to execute it. Note the organization of the program, in particular the extensive use of procedures, and how it performs the following tasks in sequence:

• declares important constants (model parameters and simulation parameters)
• loads files that other stuff will depend on
• creates the model itself (just a collection of cells that spike at random times)
• specifies instrumentation (in this case, recording of spike times)
• specifies simulation control
• executes one or more simulations with various model parameters

The solution to these problems is to give each NetStim its own random number generator.

"How?" you might very well ask.

By creating an instance of the Random class for every instance of the NetStim class, and using the NetStim class's noiseFromRandom to associate the two. You can probably figure out how to do this on your own if I just point you to a good example, like NEURON/common/netstim.hoc in the source code for entry 83319 from ModelDB http://modeldb.yale.edu/

But for the impatient, here's a quick example.

Here is the source code for two files that, taken together, demonstrate the solution.

There are several ways to generate random spike streams. One is to use a NetStim with its random parameter set to a value in the range 0 < random <= 1 (be sure to read the programmer's reference documentation of the NetStim class to find out what this does).

But what if you need more than one spike stream? Can you just add another NetStim and grind out spike times?

Not if you want your NetStims' streams to be independent of each other. Read on to find out why, and what to do about it.

There are many potential applications of randomness in modeling. These fall into two broad categories:

Randomization of the model specification
Purpose: to emulate natural biological variation of parameters such as number of cells, connectivity between cells, anatomical and/or biophysical properties of cells and their connections.

Randomization of simulation execution
Purpose: to emulate stochastic phenomena such as single channel gating, transmitter release, afferent spike trains, variation of natural stimuli, extrinsic noise sources.

In this example, the ring network created in an earlier tutorial will be used to run the model with a GPU. In addition, it is shown how to profile the performance difference. Only the differences with that tutorial will be described.

In this example, the ring network created in an earlier tutorial will be used to run the model in a distributed context using MPI. Only the differences with that tutorial will be described.

This tutorial uses a single cell and reproduces a Brian2 example. We aim to reproduce a spike timing-dependent plastivity curve which arises from stochastic calcium-based synapse dynamics described in Graupner and Brunel.

This example takes elements from other tutorials to create a geometrically detailed single cell model from an SWC morphology file, and adds predictions of extracellular potentials using the LFPykit Python library. LFPykit provides a few different classes facilitating calculations of extracellular potentials and related electroencephalography (EEG) and magnetoencephalography (MEG) signals from geometrically detailed neuron models under various assumptions. These are signals that mainly stem from transmembrane currents.