A library for deep learning with spiking neural networks
The purpose of this library is to exploit the advantages of bio-inspired neural components, who are sparse and event-driven - a fundamental difference from artificial neural networks. Norse expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components.
Example usage: template tasks
Norse comes packed with a few example tasks, such as MNIST, but is generally meant for use in specific deep learning tasks (see below section on long short-term spiking neural networks):
Norse is a machine learning library that builds on the PyTorch infrastructure. While we have a few tasks included, it is meant to be used in designing and evaluating experiments involving biologically realistic neural networks.
This readme explains how to install norse and apply it in your own experiments.
Note that this guide assumes you are on a terminal friendly environment with access to the
git commands. Python version 3.7+ is required.
Installing from PyPi
pip install norse
Installing from source
git clone https://github.com/norse/norse cd norse python setup.py install
- To train an MNIST classification network, invoke
- To train a CIFAR classification network, invoke
- To train the cartpole balancing task with Policy gradient, invoke
The default choices of hyperparameters are meant as reasonable starting points.
Example on using the library: Long short-term spiking neural networks
long short-term spiking neural networks from the paper by G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass is one interesting way to apply norse:
from norse.torch.module import LSNNLayer, LSNNCell # LSNNCell with 2 inputs and 10 outputs layer = LSNNLayer(LSNNCell, 2, 10) # 5 batch size running on CPU state = layer.initial_state(5, "cpu") # Generate data of shape [5, 2, 10] data = torch.zeros(2, 5, 2) # Tuple of output data and layer state output, new_state = layer.forward(data, state)
A number of projects exist that attempts to leverage the strength of bio-inspired neural networks, however none of them are fully integrated with modern machine-learning libraries such as Torch or Tensorflow. Norse was created for two reasons: to 1) apply findings from decades of research in practical settings, and to 2) accelerate our own research within bio-inspired learning.
The below list of projects serves to illustrate the state of the art, while explaining our own incentives to create and use norse.
- SNN toolbox
- This toolbox `automates the conversion of pre-trained analog to spiking neural networks'. The tool is solely for already trained networks and omits the (possibly platform specific) training.
- Neuron Simulation Toolkit (NEST)
- NEST constructs and evaluates highly detailed simulations of spiking neural networks. This is useful in a medical/biological sense but maps poorly to large datasets and deep learning.
- Nengo DL
- Nengo is a neuron simulator, and Nengo-DL is a deep learning network simulator that optimised spike-based neural networks based on an approximation method suggested by Hunsberger and Eliasmith (2016). This approach maps to, but does not build on, the deep learning framework Tensorflow, which is fundamentally different from incorporating the spiking constructs into the framework itself.
Please refer to the contributing.md
Norse is created by
- Christian Pehle (@GitHub cpehle), doctoral student at University of Heidelberg, Germany.
- Jens E. Pedersen (@GitHub jegp), doctoral student at KTH Royal Institute of Technology, Sweden.
LGPLv3. See LICENSE for license details.
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