# Articles

Articles about features of the Brian simulator.

# Bug hunt episode 2: a strange file appears

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 😀!

# Bug hunt episode 1: Broken LaTeX output for equations

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.

# Getting the timing right (scheduling 2)

#### Quickstart

To run the code below:
1. Click on the cell to select it.
2. Press SHIFT+ENTER on your keyboard or press the play button () in the toolbar above
Feel free to create new cells using the plus button (), or pressing SHIFT+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:

• propagating synaptic activity

# Getting the timing right (scheduling 1)

#### Quickstart

To run the code below:
1. Click on the cell to select it.
2. Press SHIFT+ENTER on your keyboard or press the play button () in the toolbar above
Feel free to create new cells using the plus button (), or pressing SHIFT+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:

• recording values with a StateMonitor

You can also watch the   screencast video on Youtube.

# Making use of Python: threshold finding with bisection

#### Quickstart

To run the code below:
1. Click on the cell to select it.
2. Press SHIFT+ENTER on your keyboard or press the play button () in the toolbar above
Feel free to create new cells using the plus button (), or pressing SHIFT+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).

# Non-standard neuron modelling: smooth pursuit eye movements

#### Quickstart

To run the code below:
1. Click on the cell to select it.
2. Press SHIFT+ENTER on your keyboard or press the play button () in the toolbar above
Feel free to create new cells using the plus button (), or pressing SHIFT+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.

# Non-standard neuron modelling: the pyloric network

#### Quickstart

To run the code below:
1. Click on the cell to select it.
2. Press SHIFT+ENTER on your keyboard or press the play button () in the toolbar above
Feel free to create new cells using the plus button (), or pressing SHIFT+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).