The NEURON simulation environment is used in laboratories and classrooms around the world for building and using computational models of neurons and networks of neurons.

CoreNEURON is a compute engine for the NEURON simulator optimised for both memory usage and computational speed. Its goal is to simulate large cell networks with a small memory footprint and optimal performance.

Thanks to the support provided by EBRAINS, NEURON and CoreNEURON are available on the Collaboratory for users to build and simulate cellular-level networks.

  • Flexible and powerful simulator of neurons and neuron networks
  • Separates biology from purely computational concerns
  • User-extendable library of biophysical mechanisms
  • CoreNEURON: Support for modern CPUs and GPUs architecture
  • CoreNEURON: Supports existing NEURON models with minimum porting efforts

Working with NEURON

NEURON is a simulation environment for modelling individual neurons and networks of neurons. It provides tools for conveniently building, managing and using models in a way that is numerically sound, and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties.

Recently, Python was adopted as an alternative interpreter. All hoc variables, procedures and functions can be accessed from Python, and vice versa. This provides a tremendous degree of flexibility for setting up the anatomical and biophysical properties of models, defining the appearance of the graphical interface, controlling simulations and plotting results.

The default graphical user interface can be used to create and exercise models that have a wide range of complexity. With the GUI, it is possible to generate publication-quality results without having to write any program code at all.

ModelDB provides an accessible location for storing and efficiently retrieving computational neuroscience models.

CoreNEURON Advantages

In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted, refactored and optimised as a library called CoreNEURON.

CoreNEURON can transparently handle all spiking network simulations using the fixed time step method, including those using gap junction coupling. Users with custom mechanisms compiled via NEURON's NMODL language can check which features are supported by CoreNEURON or how to adapt models to be compatible; see more information.

An example of a specialised use case for CoreNEURON, for large network models that cannot fit into a machine’s memory when using NEURON, the CoreNEURON workflow loads data in pieces and writes optimised data to disk. Once all the pieces have been processed, CoreNEURON can load the cached data back from disk and run the full model using substantially less memory.

Simulation execution workflows: a) original NEURON solver; b) in-memory CoreNEURON solver; c) file assisted instantiation for large networks

NMODL Framework

The NMODL Framework is an improved code generation engine for NEURON MODeling Language (NMODL) replacing the original nrnivmodl. It is designed with modern compiler and code generation techniques to:

• Provide modular tools for parsing, analysing and transforming NMODL
• Provide an easy to use, high-level Python API
• Generate optimised code for modern compute architectures including CPUs, GPUs
• Flexible to implement new simulator backends
• Support for full NMODL specification.

Our NMODL framework has been tested against a large number of previously published user models and offers high-level domain-specific optimisations and symbolic algebraic simplifications before target code generation.

NMODL code generation: a) input NMODL files loaded; b) syntax trees processed for optimisation; c) output for targeted backend.


Get Involved with the NEURON Community



Scientific articles and books that have used NEURON



Registered users and +17,000 posted messages

As of February 2021, we know of more than 2400 scientific articles and books that have used NEURON. The NEURON mailing list has about 500 subscribers, and the NEURON Forum, which was started in May 2005 and is gradually supplanting the old mailing list, has more than 1600 registered users and over 17,000 posted messages in more than 4000 discussion threads.

Since 2020, the NEURON developers organise a monthly meeting via zoom to discuss matters related to NEURON and provide updates on related projects.

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