Deep learning for spiking neural networks

Overview

A deep learning library for spiking neural networks.

Test status chat on Discord DOI

Norse aims to exploit the advantages of bio-inspired neural components, which 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.

Documentation: norse.github.io/norse/

1. Getting started

To try Norse, the best option is to run one of the jupyter notebooks on Google collab.

Alternatively, you can install Norse and run one of the included tasks such as MNIST:

python -m norse.task.mnist

2. Using Norse

Norse presents plug-and-play components for deep learning with spiking neural networks. Here, we describe how to install Norse and start to apply it in your own work. Read more in our documentation.

2.1. Installation

We assume you are using Python version 3.7+, are in a terminal friendly environment, and have installed the necessary requirements. Read more in our documentation.

Method Instructions Prerequisites
From PyPi
pip install norse
Pip
From source
pip install -qU git+https://github.com/norse/norse
Pip, PyTorch
With Docker
docker pull quay.io/norse/norse
Docker
From Conda
conda install -c norse norse
Anaconda or Miniconda

2.2. Running examples

Norse is bundled with a number of example tasks, serving as short, self contained, correct examples (SSCCE). They can be run by invoking the norse module from the base directory. More information and tasks are available in our documentation and in your console by typing: python -m norse.task.<task> --help, where <task> is one of the task names.

  • To train an MNIST classification network, invoke
    python -m norse.task.mnist
  • To train a CIFAR classification network, invoke
    python -m norse.task.cifar10
  • To train the cartpole balancing task with Policy gradient, invoke
    python -m norse.task.cartpole

Norse is compatible with PyTorch Lightning, as demonstrated in the PyTorch Lightning MNIST task variant (requires PyTorch lightning):

python -m norse.task.mnist_pl --gpus=4

2.3. Example: Spiking convolutional classifier

Open In Colab

This classifier is a taken from our tutorial on training a spiking MNIST classifier and achieves >99% accuracy.

import torch, torch.nn as nn
from norse.torch import LICell             # Leaky integrator
from norse.torch import LIFCell            # Leaky integrate-and-fire
from norse.torch import SequentialState    # Stateful sequential layers

model = SequentialState(
    nn.Conv2d(1, 20, 5, 1),      # Convolve from 1 -> 20 channels
    LIFCell(),                   # Spiking activation layer
    nn.MaxPool2d(2, 2),
    nn.Conv2d(20, 50, 5, 1),     # Convolve from 20 -> 50 channels
    LIFCell(),
    nn.MaxPool2d(2, 2),
    nn.Flatten(),                # Flatten to 800 units
    nn.Linear(800, 10),
    LICell(),                    # Non-spiking integrator layer
)

data = torch.randn(8, 1, 28, 28) # 8 batches, 1 channel, 28x28 pixels
output, state = model(data)      # Provides a tuple (tensor (8, 10), neuron state)

2.4. Example: Long short-term spiking neural networks

The long short-term spiking neural networks from the paper by G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass (2018) is another interesting way to apply norse:

import torch
from norse.torch import LSNNRecurrent
# Recurrent LSNN network with 2 input neurons and 10 output neurons
layer = LSNNRecurrent(2, 10)
# Generate data: 20 timesteps with 8 datapoints per batch for 2 neurons
data  = torch.zeros(20, 8, 2)
# Tuple of (output spikes of shape (20, 8, 2), layer state)
output, new_state = layer(data)

3. Why Norse?

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.

We are passionate about Norse: we strive to follow best practices and promise to maintain this library for the simple reason that we depend on it ourselves. We have implemented a number of neuron models, synapse dynamics, encoding and decoding algorithms, dataset integrations, tasks, and examples. Combined with the PyTorch infrastructure and our high coding standards, we have found Norse to be an excellent tool for modelling scaleable experiments and Norse is actively being used in research.

Finally, we are working to keep Norse as performant as possible. Preliminary benchmarks suggest that Norse achieves excellent performance on small networks of up to ~5000 neurons per layer. Aided by the preexisting investment in scalable training and inference with PyTorch, Norse scales from a single laptop to several nodes on an HPC cluster with little effort. As illustrated by our PyTorch Lightning example task.

Read more about Norse in our documentation.

4. Similar work

The list of projects below serves to illustrate the state of the art, while explaining our own incentives to create and use norse.

  • BindsNET also builds on PyTorch and is explicitly targeted at machine learning tasks. It implements a Network abstraction with the typical 'node' and 'connection' notions common in spiking neural network simulators like nest.
  • cuSNN is a C++ GPU-accelerated simulator for large-scale networks. The library focuses on CUDA and includes spike-time dependent plasicity (STDP) learning rules.
  • decolle implements an online learning algorithm described in the paper "Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)" by J. Kaiser, M. Mostafa and E. Neftci.
  • GeNN compiles SNN network models to NVIDIA CUDA to achieve high-performing SNN model simulations.
  • Long short-term memory Spiking Neural Networks (LSNN) is a tool from the University of Graaz for modelling LSNN cells in Tensorflow. The library focuses on a single neuron and gradient model.
  • 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. In turn, this requires manual translations into each individual backend, which influences portability.
  • Neuron Simulation Toolkit (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.
  • PyNN is a Python interface that allows you to define and simulate spiking neural network models on different backends (both software simulators and neuromorphic hardware). It does not currently provide mechanisms for optimisation or arbitrary synaptic plasticity.
  • PySNN is a PyTorch extension similar to Norse. Its approach to model building is slightly different than Norse in that the neurons are stateful.
  • Rockpool is a Python package developed by SynSense for training, simulating and deploying spiking neural networks. It offers both JAX and PyTorch primitives.
  • Sinabs is a PyTorch extension by SynSense. It mainly focuses on convolutions and translation to neuromorphic hardware.
  • SlayerPyTorch is a Spike LAYer Error Reassignment library, that focuses on solutions for the temporal credit problem of spiking neurons and a probabilistic approach to backpropagation errors. It includes support for the Loihi chip.
  • SNN 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.
  • snnTorch is a simulator built on PyTorch, featuring several introduction tutorials on deep learning with SNNs.
  • SpikingJelly is another PyTorch-based spiking neural network simulator. SpikingJelly uses stateful neurons. Example of training a network on MNIST.
  • SpyTorch presents a set of tutorials for training SNNs with the surrogate gradient approach SuperSpike by F. Zenke, and S. Ganguli (2017). Norse implements SuperSpike, but allows for other surrogate gradients and training approaches.
  • s2net is based on the implementation presented in SpyTorch, but implements convolutional layers as well. It also contains a demonstration how to use those primitives to train a model on the Google Speech Commands dataset.

5. Contributing

Contributions are warmly encouraged and always welcome. However, we also have high expectations around the code base so if you wish to contribute, please refer to our contribution guidelines.

6. Credits

Norse is created by

More information about Norse can be found in our documentation. The research has received funding from the EC Horizon 2020 Framework Programme under Grant Agreements 785907 and 945539 (HBP) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Fundation) under Germany's Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster).

7. Citation

If you use Norse in your work, please cite it as follows:

@software{norse2021,
  author       = {Pehle, Christian and
                  Pedersen, Jens Egholm},
  title        = {{Norse -  A deep learning library for spiking 
                   neural networks}},
  month        = jan,
  year         = 2021,
  note         = {Documentation: https://norse.ai/docs/},
  publisher    = {Zenodo},
  version      = {0.0.6},
  doi          = {10.5281/zenodo.4422025},
  url          = {https://doi.org/10.5281/zenodo.4422025}
}

Norse is actively applied and cited in the literature. We are keeping track of the papers cited by Norse in our documentation.

8. License

LGPLv3. See LICENSE for license details.

You might also like...
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

Code for
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

A flexible framework of neural networks for deep learning
A flexible framework of neural networks for deep learning

Chainer: A deep learning framework Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX Forum (en, ja

Code samples for my book "Neural Networks and Deep Learning"

Code samples for "Neural Networks and Deep Learning" This repository contains code samples for my book on "Neural Networks and Deep Learning". The cod

State of the Art Neural Networks for Deep Learning

pyradox This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2

A flexible framework of neural networks for deep learning
A flexible framework of neural networks for deep learning

Chainer: A deep learning framework Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX Forum (en, ja

Transfer Learning library for Deep Neural Networks.
Transfer Learning library for Deep Neural Networks.

Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon

Comments
  • Add spack package file

    Add spack package file

    This adds a spack package file that successfully built on our local spack instance… however, this should be probably reflected in your github workflow → should I just try to add it (i.e. don't merge this but I'll update this PR) :)?

    opened by muffgaga 1
Releases(0.0.1)
Owner
Electronic Vision(s) Group — BrainScaleS Neuromorphic Hardware
Kirchhoff-Institute for Physics, Ruprecht-Karls-Universität Heidelberg
Electronic Vision(s) Group — BrainScaleS Neuromorphic Hardware
Txt2Xml tool will help you convert from txt COCO format to VOC xml format in Object Detection Problem.

TXT 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Txt2Xml too

Nguyễn Trường Lâu 4 Nov 24, 2022
Seq2seq - Sequence to Sequence Learning with Keras

Seq2seq Sequence to Sequence Learning with Keras Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python dee

Fariz Rahman 3.1k Dec 18, 2022
Kaggle DSTL Satellite Imagery Feature Detection

Kaggle DSTL Satellite Imagery Feature Detection

Konstantin Lopuhin 206 Oct 29, 2022
OneFlow is a performance-centered and open-source deep learning framework.

OneFlow OneFlow is a performance-centered and open-source deep learning framework. Latest News Version 0.5.0 is out! First class support for eager exe

OneFlow 4.2k Jan 07, 2023
OBBDetection: an oriented object detection toolbox modified from MMdetection

OBBDetection note: If you have questions or good suggestions, feel free to propose issues and contact me. introduction OBBDetection is an oriented obj

MIXIAOXIN_HO 3 Nov 11, 2022
Keras Image Embeddings using Contrastive Loss

Keras-Image-Embeddings-using-Contrastive-Loss Image to Embedding projection in vector space. Implementation in keras and tensorflow for custom data. B

Shravan Anand K 5 Mar 21, 2022
An educational tool to introduce AI planning concepts using mobile manipulator robots.

JEDAI Explains Decision-Making AI Virtual Machine Image The recommended way of using JEDAI is to use pre-configured Virtual Machine image that is avai

Autonomous Agents and Intelligent Robots 13 Nov 15, 2022
Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge Introduction SentiLARE is a sentiment-aware pre-trained language

74 Dec 30, 2022
The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Patient Selection for Diabetes Drug Testing Project Overview EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical ind

Omar Laham 1 Jan 14, 2022
Video Matting via Consistency-Regularized Graph Neural Networks

Video Matting via Consistency-Regularized Graph Neural Networks Project Page | Real Data | Paper Installation Our code has been tested on Python 3.7,

41 Dec 26, 2022
Udacity's CS101: Intro to Computer Science - Building a Search Engine

Udacity's CS101: Intro to Computer Science - Building a Search Engine All soluti

Phillip 0 Feb 26, 2022
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

tricktreat 87 Dec 16, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

ChongjianGE 89 Dec 02, 2022
An implementation of MobileFormer

MobileFormer An implementation of MobileFormer proposed by Yinpeng Chen, Xiyang Dai et al. Including [1] Mobile-Former proposed in:

slwang9353 62 Dec 28, 2022
As-ViT: Auto-scaling Vision Transformers without Training

As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2

VITA 68 Sep 05, 2022
Low-dose Digital Mammography with Deep Learning

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography ====== This repository contains

WANG-AXIS 6 Dec 13, 2022
Unofficial PyTorch Implementation of AHDRNet (CVPR 2019)

AHDRNet-PyTorch This is the PyTorch implementation of Attention-guided Network for Ghost-free High Dynamic Range Imaging (CVPR 2019). The official cod

Yutong Zhang 4 Sep 08, 2022