The Nengo team is jubilant to announce the release of Nengo 2.7.0!
What is Nengo?
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.
What’s new?
Nengo 2.7.0 contains a few new features and bugfixes.
We added a SpikingRectifiedLinear neuron type, which provides an interesting balance between biological realism and computational efficiency, since it produces spikes yet is a very simple model.
We also added amplitude parameters to several neuron types, including LIF, LIFRate, and RectifiedLinear. Changing the amplitude of neural output is useful for many deep learning algorithms, or when attempting to match a Nengo model to a model implemented in another neural simulator.
We fixed several bugs related to pickling Nengo models, the decoder cache, and the config system.
To see the full list of changes in Nengo 2.7.0, head to the Github release page.
How do I get it?
To get the new version of Nengo, use pip.
pip install --upgrade nengo
Where can I learn more?
Where can I get help?
You’re already there! If you have an issue upgrading or have any other questions, please post them in this forum.