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 |
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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 |
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params = {"sion_chunksize": 1024}
nest.recording_backends = {
"sionlib": params
}
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params = {"sion_chunksize": 1024}
nest.SetDefaults("sionlib", params)
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nest.recording_backends["ascii"]
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nest.GetDefaults("ascii")
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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).