Articles about features of the Brian simulator.
This is the second article in the “bug hunt” series. In these articles, I go through a recent bug in Brian (or one of its dependencies) and describe all the steps I used to find the source of the bug and how I fixed it.
Today’s bug is about a strangely named file that seemingly appears out of nowhere when running Brian simulations. The final fix for the bug will turn out to be a single character change in the Brian code base 😀!
This article starts a new series of blog posts about “bug hunts”. In these articles, I will go through a recent bug in Brian (or one of its dependencies) and describe all the steps I used to find the source of the bug and how I fixed it. I will try to not only focus on the Brian-side of things, but also show some general tools like git bisect or “monkey patching” that can be helpful to find the source of these nasty critters (no actual bugs were harmed during the making of this blog post).
Let’s start! Today’s bug will be about equations, and more specifically about their LaTeX representation. As most of you probably know, Brian can represent equations, quantities, etc. in LaTeX. This representation can then either be included in a LaTeX document or directly rendered for example as the output in jupyter notebooks.
This article is written as a Jupyter notebook which you can execute and modify interactively.
You can either download it via the “Source” link on the top right, or run it directly in the browser on the
mybinder infrastructure:
For more information, see our general Notes on Notebooks.
SHIFT+ENTER on your keyboard or press the play button
() in the toolbar aboveSHIFT+ENTER while this cell
is selected.
“Scheduling”: mechanism to determine the order of operations during a simulation
In this video we will look at its importance for:
You can also watch the screencast video on Youtube.
This article is written as a Jupyter notebook which you can execute and modify interactively.
You can either download it via the “Source” link on the top right, or run it directly in the browser on the
mybinder infrastructure:
For more information, see our general Notes on Notebooks.
SHIFT+ENTER on your keyboard or press the play button
() in the toolbar aboveSHIFT+ENTER while this cell
is selected.
“Scheduling”: mechanism to determine the order of operations during a simulation
In this notebook we will look at its importance for:
StateMonitor
You can also watch the screencast video on Youtube.
This article is written as a Jupyter notebook which you can execute and modify interactively.
You can either download it via the “Source” link on the top right, or run it directly in the browser on the
mybinder infrastructure:
For more information, see our general Notes on Notebooks.
SHIFT+ENTER on your keyboard or press the play button
() in the toolbar aboveSHIFT+ENTER while this cell
is selected.
This article demonstrates how a control flow, where simulation parameters depend on the results of previous simulations, can be expressed by making use of standard control structures in Python. By having access to the full expressivity of a general purpose programming language, expressing such control flow is straight-forward; this would not be the case for a declarative model description.
Our goal in this toy example is to find the threshold voltage of neuron as a function of the density of sodium channels.
This example is from our eLife paper (Stimberg et al. 2019).
This article is written as a Jupyter notebook which you can execute and modify interactively.
You can either download it via the “Source” link on the top right, or run it directly in the browser on the
mybinder infrastructure:
For more information, see our general Notes on Notebooks.
SHIFT+ENTER on your keyboard or press the play button
() in the toolbar aboveSHIFT+ENTER while this cell
is selected.
In this article we demonstrate how Brian can be used to simulate non-neural aspects of the model. This is an idealized model of the smooth pursuit reflex, including two ocular muscles, a moving visual stimulus and spiking neural control.
This article is adapted from our eLife paper (Stimberg et al. 2019), which includes an interactive version that you can play with here.
This article is written as a Jupyter notebook which you can execute and modify interactively.
You can either download it via the “Source” link on the top right, or run it directly in the browser on the
mybinder infrastructure:
For more information, see our general Notes on Notebooks.
SHIFT+ENTER on your keyboard or press the play button
() in the toolbar aboveSHIFT+ENTER while this cell
is selected.
One of the great advantages of using Brian is that defining new non-standard model types is easy. In this article, we will build a highly simplified model of the pyloric circuit of the crustacean stomatogastric ganglion. This circuit generates a tri-phasic rhythmic pattern with alternating bursts of action potentials in different types of motor neurons. Here, we follow previous work (e.g. Golowasch et al., 1999) by modeling the circuit as consisting of three populations: AB/PD (anterior buster and pyloric dilator neurons), LP (lateral pyloric neurons), and PY (pyloric neurons). This model has a number of non-standard properties that will be described in the following annotated version of the code.
Golowasch, J., Casey, M., Abbott, L. F., & Marder, E. (1999).
Network Stability from Activity-Dependent Regulation of Neuronal Conductances.
Neural Computation, 11(5), 1079-1096.
https://doi.org/10.1162/089976699300016359
This article was based on one of the examples from our eLife paper (Stimberg et al. 2019).
The articles in this blog are written as Jupyter notebooks – interactive documents that contain text, Python code, and the results of running the code (i.e. text or figures). This article gives some details on the various ways to use them and explains common commands we use when presenting Brian code.
While the documents are “static” on this website, i.e. you can neither change nor run the code, you have two options to interactively explore and modify their content: