Articles

Articles about features of the Brian simulator.

Making use of Python: threshold finding with bisection

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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).

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Non-standard neuron modelling: smooth pursuit eye movements

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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.

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Non-standard neuron modelling: the pyloric network

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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).

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Notes on Notebooks

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:

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