Tutorials & E-Library

This example follows the same general approach described for segmenting simulations of a model cell, but the data to be saved is a record of spike times and the associated unique identifiers of the spike sources. The conceptual model is very simple: a spontaneously active cell that fires spikes at random intervals.

When I'm trying something new, I find that it helps to keep everything as simple as possible, so I can focus all of my effort on the real problem. Using toy models with minimal instrumentation saves time and effort, and keeping run times short speeds up debugging and eliminates the need to wade through piles of numbers.

For these reasons, my conceptual model is merely a spherical passive cell, with surface area 100 um2, driven by a current clamp that injects a constant current. The variable to be recorded is membrane potential. The model's membrane time constant and the duration of the injected current should be long enough that membrane potential will change gradually from the time the current pulse starts until the end of the simulation. This ensures that mismatches at the boundaries between adjacent simulation segments would be revealed by obvious, abrupt changes in the time course of recorded membrane potential.

In this part you will start to implement recording and saving of a segmented simulation, I followed a pattern similar to what worked for a full run.

Create a hoc file called netrecord_segrun.hoc that contains statements that execute a user-specified number of simulation segments and save each segment's results to a separate file.

Copy initnet_fullrun.hoc to initnet_segrun.hoc, and edit this new file to change the load_file("netrecord_fullrun.hoc") statement to load_file("netrecord_segrun.hoc").

This tutorial guides you through the simulation of a model cell in Neuron through toy models with minimal instrumentation. In this second step you will learn how to easily implement recording and saving of a full run.

This tutorial guides you through the simulation of a model cell in Neuron through toy models with minimal instrumentation. In this first step you will learn how to implement and test the model.

Reconstituting the results of a complete simulation from the segmentxx.dat files.

Reconstituting the results of a complete simulation from the segmentxx.dat files.

To implement recording and saving of a segmented simulation, I followed a pattern similar to what worked for a full run.

Create a hoc file called cellrecord_segrun.hoc that contains statements that execute a user-specified number of simulation segments and save each segment's results to a separate file.

Copy initcell_fullrun.hoc to initcell_segrun.hoc, and edit this new file to change the load_file("cellrecord_fullrun.hoc") statement to load_file("cellrecord_segrun.hoc").

It was very easy to implement recording and saving of a complete simulation of the network.

Create a hoc file called netrecord_fullrun.hoc that contains statements to accomplish these tasks:

launch a simulation (netrig.hoc already takes care of recording spike times and cell IDs to a pair of Vectors)
write the results to a file

Add a load_file("netrecord_fullrun.hoc") statement to the end of a copy of initnet_basic.hoc, and save this as initnet_fullrun.hoc

The EBRAINS Live Papers are structured and interactive supplementary documents to complement journal publications, that allow users to readily access, explore, and utilize the various kinds of data underlying scientific studies. Interactivity is a prominent feature with several integrated tools and services that will allow users to download, visualize, or simulate data, models, and results presented in the corresponding publications.

In this video, we provide a quick overview of the purpose of EBRAINS Live Papers.

The EBRAINS Live Papers are structured and interactive supplementary documents to complement journal publications, that allow users to readily access, explore, and utilize the various kinds of data underlying scientific studies. Interactivity is a prominent feature with several integrated tools and services that will allow users to download, visualize, or simulate data, models, and results presented in the corresponding publications.

In this tutorial-cum-demonstration, you will learn how to create a Collab on the EBRAINS Collaboratory and basically everything that there is to learn about creating a new Live Paper.

Siibra Explorer is bridging the gap between human brain mapping and accessible exploration of the Julich Brain Atlas.