ngclearn 1.0b0

Simulation software for building and analyzing arbitrary predictive coding, spiking network, and biomimetic neural systems.

ngc-learn is a Python library for building, simulating, and analyzing biomimetic systems, neurobiological agents, spiking neuronal networks, predictive coding circuitry, and models that learn via biologically-plausible forms of credit assignment. This simulation toolkit is built on top of JAX and is distributed under the 3-Clause BSD license.

It is currently maintained by the Neural Adaptive Computing (NAC) laboratory.

Documentation

Official documentation, including tutorials, can be found here.

The official blog-post related to the source paper behind this software library can be found here.
You can find the related paper right here, which was selected to appear in the Nature Neuromorphic Hardware and Computing Collection in 2023 and was chosen as one of the Editors' Highlights for Applied Physics and Mathematics in 2022.

Installation

Dependencies

ngc-learn requires:

1. Python (>=3.10)
2. NumPy (>=1.26.0)
3. SciPy (>=1.7.0)
4. ngcsimlib (>=0.2.b1), (visit official page here)
5. JAX (>= 0.4.18) (to enable GPU use, make sure to install one of the CUDA variants)

---

ngc-learn 1.0.beta0 and later require Python 3.10 or newer as well as ngcsimlib >=0.2.b1. ngc-learn's plotting capabilities (routines within ngclearn.utils.viz) require Matplotlib (>=3.8.0) and imageio (>=2.31.5) and both plotting and density estimation tools (routines within ngclearn.utils.density) will require Scikit-learn (>=0.24.2). Many of the tutorials will require Matplotlib (>=3.8.0), imageio (>=2.31.5), and Scikit-learn (>=0.24.2).

User Installation

Setup: The easiest way to install ngc-learn (CPU version) is through pip:

$ python install ngclearn

The documentation includes more detailed installation instructions. Note that this library was developed on Ubuntu 20.04 and tested on Ubuntu(s) 18.04 and 20.04.

If the installation was successful, you should see the following if you test it against your Python interpreter, i.e., run the $ python command and complete the following sequence of steps as depicted in the screenshot below:

Note: For access to the previous Tensorflow-2 version of ngc-learn (of which we no longer support), please visit the repo for ngc-learn-legacy.

Attribution:

If you use this code in any form in your project(s), please cite its source paper (as well as ngc-learn's official software citation):

@article{Ororbia2022,
  author={Ororbia, Alexander and Kifer, Daniel},
  title={The neural coding framework for learning generative models},
  journal={Nature Communications},
  year={2022},
  month={Apr},
  day={19},
  volume={13},
  number={1},
  pages={2064},
  issn={2041-1723},
  doi={10.1038/s41467-022-29632-7},
  url={https://doi.org/10.1038/s41467-022-29632-7}
}

Development:

We warmly welcome community contributions to this project. For details on how to make a contribution to ngc-learn, please see our contributing guidelines.

Source Code You can check/pull the latest source code for this library via:

$ git clone https://github.com/NACLab/ngc-learn.git

If you are working on and developing with ngc-learn pulled from the github repo, then run the following command to set up an editable install:

$ python install -e .

Version:
1.0.0-Beta

Author: Alexander G. Ororbia II
Director, Neural Adaptive Computing (NAC) Laboratory
Rochester Institute of Technology, Department of Computer Science

Copyright:

Copyright (C) 2021 The Neural Adaptive Computing Laboratory - All Rights Reserved
You may use, distribute and modify this code under the terms of the BSD 3-clause license.

You should have received a copy of the BSD 3-clause license with this software.
If not, please email us