Tagneuron

How to use Neuronvisio to select particular sections of a NEURON based multi-compartment model

Displaying and visualizing selected sections in multi-compartmental models in 3D it’s quite an hard task, however the info and the clarity that is possible to achieve is worth the effort.

Let’s say you have your multi-compartiment model ready in NEURON and you are interested to show in 3D selected sections of the model with arbitrary colors (for example, to show where certain stimuli are applied), like I did in my Medium Spiny Neuron model to show which spines get stimulated:

spiny MSN with stimulated with different trains in selected spines

spiny MSN with stimulated with different trains in selected spines

Neuronvisio offers a nice API, from the visio module, accessible as controls.visio.select_sections(secs_list, scalar_value), which makes this operation easy.

For example, let’s take the pyramidal neuron model that comes as an example with Neuronvisio.

Let’s say that we want to select the soma, the iseg and the first section of the myelin, and we want to give them arbitrary colors.

To achieve that we can easily run:

controls.visio.select_sections([“soma”, “iseg”, “myelin[0]”], [1, 0.5, 0.2]), obtaining this picture:

Selecting and coloring special sections with Neuronvisio

Selecting and coloring special sections with Neuronvisio

Quite handy and pretty fast, don’t you think?

Some thoughts about NeuroML and standardization

I’m pleased to say that we have released Neuronvisio 0.8.5, which has several small improvements and better documentation. I’m pretty sure that there is always room to improve the documentation.

Anyway, first of all the cerebellum network rendered in Neuronvisio, with the model taken from the NeuroML website

cerebellum_network

Cerebellum Network, taken from the NeuroML example pack

The idea with this release was to demonstrate the ability of Neuronvisio to visualize also Network models which are instantiated in NEURON. To note, this ability has been there from version 0.1.0, but now we are beaming on it.

Neuronvisio is able to import 3 different files: hdf5 structured in Neuronvisio format, hoc files, and NeuroML.

The first file is our way to approach hdf5, which gives us the ability to save all the data of a simulation, plus the model itself in one single file, which can then be reloaded and moved. We do all the work here.

The other two are files format where Neuronvisio does not import directly, but let’s NEURON do the heavy-lifting, to avoid any duplication. The hoc format is classic NEURON interpreted script, while NeuroML is an effort to standardize the way neuronal model are encoded.

In particular I would like to stress the last point: Neuronvisio does not have an ad-hoc NeuroML importer, but re-use the one provided by NEURON. We now just exposing an easy way to load a NeuroML file directly in Neuronvisio with the load method from the Controls class

from neuronvisio.controls import Controls
controls = Controls() # starting the GUI
controls.load('path/to/my_model.xml') #or .h5 or .hoc

or directly launching the program, if you prefer

$ neuronvisio [path/to/my_model.xml] # or .h5 or .hoc

this gives us one powerful interface to simplify the life of a user.

Of course, if your model is in python, you can always run it within Ipython as standard python file

$ neuronvisio
In [1]: run path/to/my_model_in_python

The only problem is the NeuroML importer from NEURON does not handle properly the Network file of NeuroML, and this has been registered as issue #50 on our bug-tracker. This belong more to NEURON then Neuronvisio, but they don’t have a bug-tracker, so we logged on ours tracker and we will link any other place where the discussion will take place. NEURON has an amazing forum with quite instantaneous answers from the NEURON community, including Hines and Carnevale, and we will bring the issue over there.

So, we didn’t write our NeuroML importer, because there is no point to replicate what a software already does. That’s why we are now collaborating with the writing of the libNeuroML library, to have one good library that permits to load any NeuroML model properly, and then give the ability to the developer to map it to its own data-structure.

This is the same approach used in the SBML community, which I think is very powerful.

P.S.: So how did we manage to load the Network in NEURON and visualize it in Neuronvisio, if the NEURON (sorry, NEURON has to be written all capital to be precise…) NeuroML importer is not up to the job yet? We have used the neuroConstruct program, which is able to export a model to NEURON, and used the hoc files to load the model up.

Neuronvisio ModelDb plugged in released into the wild

We have just released Neuronvisio 0.7.0.

With this release it is possible to browse the models present on the ModelDb database, and have a look at the readme and at the properties of the Model.

Model Information and properties are presented in a quick way to the user

The Load Model button permits to download, extract, compile and load the model in one click. Sweet.

The other big things is that I didn’t write all this code, but actually 0.7.0 it’s the first release that features a contribution from another person (before was one man band!). Uri wrote the scraper for ModelDb and I’ve hooked it together in the GUI. We developed using the pull-request framework, which github makes very nice and clean.

If you’re interested in computational Neuroscience, and you are using NEURON, give Neuronvisio a go.

Say hello to NeuronVisio

Today I released the first public release 0.1.0 of NeuronVisio.

NeuronVisio is a GTK2 user interface for NEURON simulator.

NeuronVisio connects with NEURON using the new python NEURON interface.

  • 3D visualization of the model with the possibility to change it runtime
  • Creation of vectors to record any variable present in the section
  • Pylab integration to plot directly the result of the simulation
  • Explore of the timecourse of any variable among time using a color coded scale in the 3d representation
  • the GUI runs in its own thread so it’s possible to use the console to modify/interact with the model.

pyramidal neuron model

The code is on github (which I discover create automatically a tarball for you when you tag the package… sweet 🙂 )

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