The Brian spiking neural network simulator (Posts about model)

Making use of Python: threshold finding with bisection

Brian team — Thu, 30 Jan 2020 17:46:00 GMT

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:

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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).

Non-standard neuron modelling: smooth pursuit eye movements

Brian team — Thu, 30 Jan 2020 17:07:00 GMT

For more information, see our general Notes on Notebooks.

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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.

Non-standard neuron modelling: the pyloric network

Brian team — Tue, 28 Jan 2020 16:46:00 GMT

For more information, see our general Notes on Notebooks.

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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).