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https://github.com/jeshraghian/snntorch

Deep and online learning with spiking neural networks in Python
https://github.com/jeshraghian/snntorch

machine-learning neural-networks neuron-models neuroscience python pytorch snn spike spiking spiking-neural-networks

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Deep and online learning with spiking neural networks in Python

Lists

README 

        
================

Introduction

================



.. image:: https://github.com/jeshraghian/snntorch/actions/workflows/build.yml/badge.svg

        :target: https://snntorch.readthedocs.io/en/latest/?badge=latest



.. image:: https://readthedocs.org/projects/snntorch/badge/?version=latest

        :target: https://snntorch.readthedocs.io/en/latest/?badge=latest

        :alt: Documentation Status



.. image:: https://img.shields.io/discord/906036932725841941

        :target: https://discord.gg/cdZb5brajb

        :alt: Discord



.. image:: https://img.shields.io/pypi/v/snntorch.svg

         :target: https://pypi.python.org/pypi/snntorch



.. image:: https://img.shields.io/conda/vn/conda-forge/snntorch.svg

        :target: https://anaconda.org/conda-forge/snntorch



.. image:: https://static.pepy.tech/personalized-badge/snntorch?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads

        :target: https://pepy.tech/project/snntorch



.. image:: https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/snntorch_alpha_scaled.png?raw=true

        :align: center

        :width: 700



The brain is the perfect place to look for inspiration to develop more efficient neural networks. One of the main differences with modern deep learning is that the brain encodes information in spikes rather than continuous activations. 

snnTorch is a Python package for performing gradient-based learning with spiking neural networks.

It extends the capabilities of PyTorch, taking advantage of its GPU accelerated tensor 

computation and applying it to networks of spiking neurons. Pre-designed spiking neuron models are seamlessly integrated within the PyTorch framework and can be treated as recurrent activation units. 



.. image:: https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/spike_excite_alpha_ps2.gif?raw=true

        :align: center

        :width: 800



If you like this project, please consider starring ⭐ this repo as it is the easiest and best way to support it.



If you have issues, comments, or are looking for advice on training spiking neural networks, you can open an issue, a discussion, or chat in our `discord `_ channel.



snnTorch Structure

^^^^^^^^^^^^^^^^^^^^^^^^

snnTorch contains the following components: 



.. list-table::

   :widths: 20 60

   :header-rows: 1



   * - Component

     - Description

   * - `snntorch `_

     - a spiking neuron library like torch.nn, deeply integrated with autograd

   * - `snntorch.export `_

     - enables cross-compatibility with other SNN libraries via `NIR `_

   * - `snntorch.functional `_

     - common arithmetic operations on spikes, e.g., loss, regularization etc.

   * - `snntorch.spikegen `_

     - a library for spike generation and data conversion

   * - `snntorch.spikeplot `_

     - visualization tools for spike-based data using matplotlib and celluloid

   * - `snntorch.surrogate `_

     - optional surrogate gradient functions

   * - `snntorch.utils `_

     - dataset utility functions



snnTorch is designed to be intuitively used with PyTorch, as though each spiking neuron were simply another activation in a sequence of layers. 

It is therefore agnostic to fully-connected layers, convolutional layers, residual connections, etc. 



At present, the neuron models are represented by recursive functions which removes the need to store membrane potential traces for all neurons in a system in order to calculate the gradient. 

The lean requirements of snnTorch enable small and large networks to be viably trained on CPU, where needed. 

Provided that the network models and tensors are loaded onto CUDA, snnTorch takes advantage of GPU acceleration in the same way as PyTorch. 



Citation 

^^^^^^^^^^^^^^^^^^^^^^^^

If you find snnTorch useful in your work, please cite the following source:



`Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish

Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu “Training

Spiking Neural Networks Using Lessons From Deep Learning”. Proceedings of the IEEE, 111(9)

September 2023. `_



.. code-block:: bash



  @article{eshraghian2021training,

          title   =  {Training spiking neural networks using lessons from deep learning},

          author  =  {Eshraghian, Jason K and Ward, Max and Neftci, Emre and Wang, Xinxin 

                      and Lenz, Gregor and Dwivedi, Girish and Bennamoun, Mohammed and 

                     Jeong, Doo Seok and Lu, Wei D},

          journal = {Proceedings of the IEEE},

          volume  = {111},

          number  = {9},

          pages   = {1016--1054},

          year    = {2023}

  }



Let us know if you are using snnTorch in any interesting work, research or blogs, as we would love to hear more about it! Reach out at [email protected].



Requirements 

^^^^^^^^^^^^^^^^^^^^^^^^

The following packages need to be installed to use snnTorch:



* torch >= 1.1.0

* numpy >= 1.17

* pandas

* matplotlib

* math

* nir

* nirtorch



They are automatically installed if snnTorch is installed using the pip command. Ensure the correct version of torch is installed for your system to enable CUDA compatibility. 



Installation

^^^^^^^^^^^^^^^^^^^^^^^^



Run the following to install:



.. code-block:: bash



  $ python

  $ pip install snntorch



To install snnTorch from source instead::



  $ git clone https://github.com/jeshraghian/snnTorch

  $ cd snntorch

  $ python setup.py install



To install snntorch with conda::



    $ conda install -c conda-forge snntorch



To install for an Intelligent Processing Units (IPU) based build using Graphcore's accelerators::



  $ pip install snntorch-ipu

    



API & Examples 

^^^^^^^^^^^^^^^^^^^^^^^^

A complete API is available `here `__. Examples, tutorials and Colab notebooks are provided.



Quickstart 

^^^^^^^^^^^^^^^^^^^^^^^^



.. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/quickstart.ipynb



Here are a few ways you can get started with snnTorch:



* `Quickstart Notebook (Opens in Colab)`_



* `The API Reference`_ 



* `Examples`_



* `Tutorials`_



.. _Quickstart Notebook (Opens in Colab): https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/quickstart.ipynb

.. _The API Reference: https://snntorch.readthedocs.io/

.. _Examples: https://snntorch.readthedocs.io/en/latest/examples.html

.. _Tutorials: https://snntorch.readthedocs.io/en/latest/tutorials/index.html



For a quick example to run snnTorch, see the following snippet, or test the quickstart notebook:



.. code-block:: python



  import torch, torch.nn as nn

  import snntorch as snn

  from snntorch import surrogate

  from snntorch import utils



  num_steps = 25 # number of time steps

  batch_size = 1 

  beta = 0.5  # neuron decay rate 

  spike_grad = surrogate.fast_sigmoid() # surrogate gradient



  net = nn.Sequential(

        nn.Conv2d(1, 8, 5),

        nn.MaxPool2d(2),

        snn.Leaky(beta=beta, init_hidden=True, spike_grad=spike_grad),

        nn.Conv2d(8, 16, 5),

        nn.MaxPool2d(2),

        snn.Leaky(beta=beta, init_hidden=True, spike_grad=spike_grad),

        nn.Flatten(),

        nn.Linear(16 * 4 * 4, 10),

        snn.Leaky(beta=beta, init_hidden=True, spike_grad=spike_grad, output=True)

        )



  data_in = torch.rand(num_steps, batch_size, 1, 28, 28) # random input data

  spike_recording = [] # record spikes over time

  utils.reset(net) # reset/initialize hidden states for all neurons



  for step in range(num_steps): # loop over time

      spike, state = net(data_in[step]) # one time step of forward-pass

      spike_recording.append(spike) # record spikes in list



A Deep Dive into SNNs

^^^^^^^^^^^^^^^^^^^^^^^^^^^

If you wish to learn all the fundamentals of training spiking neural networks, from neuron models, to the neural code, up to backpropagation, the snnTorch tutorial series is a great place to begin.

It consists of interactive notebooks with complete explanations that can get you up to speed.



.. list-table::

   :widths: 20 60 30

   :header-rows: 1



   * - Tutorial

     - Title

     - Colab Link

   * - `Tutorial 1 `_

     - Spike Encoding with snnTorch

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_1_spikegen.ipynb



   * - `Tutorial 2 `_

     - The Leaky Integrate and Fire Neuron

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_2_lif_neuron.ipynb



   * - `Tutorial 3 `_

     -  A Feedforward Spiking Neural Network

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_3_feedforward_snn.ipynb



   * - `Tutorial 4 `_

     -  2nd Order Spiking Neuron Models (Optional)

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_4_advanced_neurons.ipynb



  

   * - `Tutorial 5 `_

     -  Training Spiking Neural Networks with snnTorch

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_5_FCN.ipynb

   



   * - `Tutorial 6 `_

     - Surrogate Gradient Descent in a Convolutional SNN

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_6_CNN.ipynb



   * - `Tutorial 7 `_

     - Neuromorphic Datasets with Tonic + snnTorch

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_7_neuromorphic_datasets.ipynb



.. list-table::

   :widths: 70 40

   :header-rows: 1



   * - Advanced Tutorials

     - Colab Link



   * - `Population Coding `_

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_pop.ipynb



   * - `Regression: Part I - Membrane Potential Learning with LIF Neurons `_

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_regression_1.ipynb



   * - `Regression: Part II - Regression-based Classification with Recurrent LIF Neurons `_

     - .. image:: https://colab.research.google.com/assets/colab-badge.svg

        :alt: Open In Colab

        :target: https://colab.research.google.com/github/jeshraghian/snntorch/blob/master/examples/tutorial_regression_2.ipynb



   * - `Accelerating snnTorch on IPUs `_

     -       —



Intelligent Processing Unit (IPU) Acceleration

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



snnTorch has been optimized for `Graphcore's IPU accelerators `_. 

To install an IPU based build of snnTorch::



  $ pip install snntorch-ipu



Low-level custom operations for IPU compatibility will be automatically compiled when :code:`import snntorch` is called for the first time. 



When updating the Poplar SDK, these operations may need to be recompiled. 

This can be done by reinstalling :code:`snntorch-ipu`, or deleting files in the base directory with an .so extension.



The :code:`snntorch.backprop` module, and several functions from :code:`snntorch.functional` and :code:`snntorch.surrogate`, are incompatible with IPUs, but can be recreated using PyTorch primitives.



Additional requirements include:



* poptorch 

* The Poplar SDK 



Refer to `Graphcore's documentation `_ for installation instructions of poptorch and the Poplar SDK.



The homepage for the snnTorch IPU project can be found `here `__.

A tutorial for training SNNs is provided `here `__.



Contributing

^^^^^^^^^^^^^^^^^^^^^^^^

If you're ready to contribute to snnTorch, instructions to do so can be `found here`_.



.. _found here: https://snntorch.readthedocs.io/en/latest/contributing.html



Acknowledgments

^^^^^^^^^^^^^^^^^^^^^^^^

snnTorch is currently maintained by the `UCSC Neuromorphic Computing Group `_. It was initially developed by `Jason K. Eshraghian`_ in the `Lu Group (University of Michigan)`_. 



Additional contributions were made by `Vincent Sun `_, `Peng Zhou `_, `Ridger Zhu `_, `Alexander Henkes `_, `Steven Abreu `_, Xinxin Wang, Sreyes Venkatesh, and Emre Neftci.



.. _Jason K. Eshraghian: https://jasoneshraghian.com

.. _Lu Group (University of Michigan): https://lugroup.engin.umich.edu/



License & Copyright

^^^^^^^^^^^^^^^^^^^^^^^^

snnTorch source code is published under the terms of the MIT License. 

snnTorch's documentation is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License (`CC BY-SA 3.0 `_).