The Nengo team is hyped to announce the release of Nengo 2.6.0!
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 2.6.0 contains several new features and bugfixes.
We added a new
NoSolver solver for situations
in which you want to skip the decoder solving process,
but don’t want to pass in a full connection weight matrix.
NoSolver should be especially useful
when learning decoders with online learning
across several trials.
We have added a warning when the simulator
is run for 0 timesteps.
This can sometimes happen when running
one timestep at a time if the
dt is changed to a higher value.
We have also raised the minimum required version of NumPy to 1.8 to take advantage of some more recent NumPy features. If you’re still using NumPy 1.7 or below, we recommend upgrading to a newer version of NumPy.
Finally, the Nengo team grew this release. Welcome to the team, Allen Wang!
To see the full list of changes in Nengo 2.6.0, 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.