What’s new in NEST 3.5

This page contains a summary of important breaking and non-breaking changes from NEST 3.4 to NEST 3.5. In addition to the release notes on GitHub, this page also contains transition information that helps you to update your simulation scripts when you come from an older version of NEST.

If you transition from an earlier version, please see our extensive transition guide from NEST 2.x to 3.0 and the list of updates for previous releases in the 3.x series.

NEST supports the SONATA format

The SONATA (Scalable Open Network Architecture TemplAte) format provides a framework for storage and exchange of network models and simulation configurations.

NEST now supports building and simulating networks of point neurons described by this SONATA format.

See our docs to learn more:

• The NEST SONATA guide for all the details

• An example SONATA script

• PyNEST API documentation for the SonataNetwork class

Run PyNEST examples as notebooks - installation free

Using the EBRAINS JupyterHub service, you can now run the PyNEST examples as Jupyter Notebooks with a click of a button.

No need to install NEST or other packages, the EBRAINS environment has everything you already need.

Explore the PyNEST examples and try it out!

New docs for high performance computing (HPC)

We have new documentation all about optmizing performance of NEST on HPC systems.

Learn about creating a job script, MPI processes and threading. We also have new info on benchmarking NEST.

Check it out:

• Optimize performance of HPC Systems

• Benchmarking NEST

New model: spike_train_injector

The spike_train_injector emits spikes at prescribed spike times which are given as an array.

We recommend its use in multi-threaded simulations where spike-emitting neurons, in a somewhat large external population, are modeled on an individual basis.

It was created to prevent an unwanted increase in memory consumption with replication at each virtual process, which happened when external neurons were modeled as a spike_generator.