What’s new in NEST 3.3

This page contains a summary of important breaking and non-breaking changes from NEST 3.2 to NEST 3.3. 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 a version earlier than 3.2, please see our extensive transition guide from NEST 2.x to 3.0 or release updates for previous releases in 3.x.

Model defaults

The model parameter delta_tau in the correlation_detector, correlomatrix_detector, and correlospinmatrix_detector, as well as dt in the noise_generator are now automatically adjusted and made compatible with a newly set simulation resolution to avoid errors when those models are instantiated. Moreover, the default value for delta_tau in the correlation_detector has been changed from 1.0 ms to 5 times the simulation resolution, in order to be consistent with the documentation of the device.

Retrieve available node and synapse models

The PyNEST function Models() is now deprecated and will be removed in a future version of NEST. Where you previously used the function nest.Models("nodes") to acquire the list of available node models, you would now write nest.node_models instead. The list of available synapse models can be retrieved using the kernel attribute nest.synapse_models. Filtering can easily and explicitly be implemented using a conditional expression in a list comprehension.

NEST 3.2

NEST 3.3





nest.Models(mtype="nodes", sel="iaf")

[m for m in nest.node_models if "iaf" in m]

New kernel attributes

On the SLI level, the individual dictionaries connruledict, growthcurvedict, modeldict, and synapsedict have been removed. Their content is now consistently available as kernel attributes with the names connection_rules, growth_curves, node_models, and synapse_models. Moreover, the list of available stimulation backends has been added under the attribute stimulation_backends.

In the course of adding the new kernel attributes, the functions Models() and ConnectionRules() of PyNEST have been marked as deprecated and will be removed in a later version.

Global properties for recording backends

The functions :py:func`.GetDefaults` and :py:func`.SetDefaults` have been extended to also work on the global properties of recording backends. This new mechanism replaces backend property access via nested dictionaries and leads to simpler and more readable code:

NEST 3.2

NEST 3.3

params = {"sion_chunksize": 1024}
nest.recording_backends = {
    "sionlib": params
params = {"sion_chunksize": 1024}
nest.SetDefaults("sionlib", params)

Compartmental models

A compartmental modelling framework has been added. The layout of the model is user-configurable at runtime, and can be adapted to represent any dendritic and/or axonal structure. By default, there are two ion channels, one Na-channel and one K-channel, and four receptor types (AMPA, GABA, NMDA and AMPA+NMDA).