Neural Microcircuit Simulation and Analysis Toolkit (NMSAT)

Image copyright: GPL, Renato Duarte, Barna Zajzon, & Abigail Morrison. (2017). Neural Microcircuit Simulation and Analysis Toolkit (0.1). Zenodo.

Tailor-made python package to build, simulate and analyse neuronal microcircuit models with PyNEST.

Publication: doi:10.5281/zenodo.582645

Website: NMSAT


Image copyright: CC-BY; Fig 1; Blundell I, Plotnikov D, Eppler JM and Morrison A (2018) Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models. Front. Neuroinform. 12:50. doi: 10.3389/fninf.2018.00050

NESTML is a domain-specific language that supports the specification of neuron models in a precise and concise syntax, based on the syntax of Python. Model equations can either be given as a simple string of mathematical notation or as an algorithm written in the built-in procedural language. The equations are analyzed by the associated toolchain, written in Python, to compute an exact solution if possible or use an appropriate numeric solver otherwise.


Website: NESTML

NEST simulator

The Neural Simulation Tool NEST is a computer program for simulating large heterogeneous networks of point neurons or neurons with a small number of compartments. NEST is best suited for models that focus on the dynamics, size, and structure of neural systems rather than on the detailed morphological and biophysical properties of individual neurons.

Publications: doi:10.4249/scholarpedia.1430

Website: NEST simulator

Elephant (Electrophysiology Analysis Toolkit)

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for parallel spike train data and time series recordings, such as the local field potentials (LFP) or intracellular voltages.

Website: ELEPHANT (Electrophisiology Analysis Toolkit)

Network Unit

Image copyright: CC-BY; Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., Denker, M., 2018. Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90.

NetworkUnit is a library based on SciUnit to perform model validation testing on the level of the statistics exhibited by the population dynamics of parallel spike and LFP data. Based on capabilities of a given model, it computes common statistical measures on the model and experimental data, and evaluates the level of agreement between the two based on comparing the measures.

Publications: doi:10.3389/fninf.2018.00090

Website: NetworkUnit

odML Tables

odMLtables is a tool to support working with metadata collections for electrophysiological data. It provides a set of library functions as well as a graphical user interface that offers to swtich between hierarchical and flat (tablular) representations of their metadata collection, and provides corresponding functions that assist in working with odML and spreadsheet files.

Publications: doi:10.3389/fninf.2019.00062

Website: odMLtables

Meanfield Toolbox

Image copyright: Layer, Moritz, Senk, Johanna, Essink, Simon, Korvasová, Karolína, van Meegen, Alexander, Bos, Hannah, … Helias, Moritz. (2020, February 10). LIF Meanfield Tools (Version v0.2). Zenodo.; Courtesy- Moritz Helias

Using this package, you can easily calculate quantities like firing rates, power spectra, and many more, which give you a deeper and more intuitive understanding of what your network does. If your network is not behaving the way you want it to, these tools might help you to figure out, or even tell you, what you need to change in order to achieve the desired behaviour. It is easy to store (and in the future, to plot) results and reuse them for further analyses.


Website: Meanfield Toolbox


Image Copyright: Figure 5C of Albers et al. (2021), arXiv:2112.09018 [q-bio.NC]

beNNch is a software framework implementing a unified, modular workflow for configuring, executing, and analyzing performance benchmarks of neuronal network simulations on high-performance computing systems.


Website: beNNch

Last Modified: 24.05.2022