The Nengo team is elated to announce the release of Nengo 2.3!
Nengo is a Python library for building and simulating large-scale neural models for AI and robotics. It can be thought of as a neural compiler, transforming a functional description of a neural model to a network of spiking or non-spiking neurons that can run on multiple backends including GPUs and neuromorphic hardware.
Nengo 2.3 features several improvements under the hood
which should speed up certain operations,
such as accessing probe data via
One nice new feature is the ability to
copy and pickle Nengo objects.
Nengo objects like
Ensemble now have
copy method that will clone that ensemble
in the current network context.
If you created a model in a console session,
for example, you can pickle that model
to a file using
then load it back up anywhere else
model = pickle.load(file).
We have also added a progress bar for models that take a long time to build. Previously, we showed a progress bar when simulating large models, but not when building them. Now you will get a progress bar for each step.
The Nengo team also grew this release. Welcome to the team, Ben Morcos!
To see the full list of changes in Nengo 2.3, head to the Github release page.
To get the new version of Nengo, use
pip install --upgrade nengo
Please come to the Nengo forum! We welcome any questions and suggestions you might have, and invite you to share your Nengo creations there.