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awesome-spiking-neural-networks

A curated list of materials for Spiking Neural Networks, 3rd generation of artificial neural networks.
https://github.com/vvvityaaa/awesome-spiking-neural-networks

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Papers :page_with_curl:
- Neuromorphic hardware
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - A Survey of Neuromorphic Computing and Neural Networks in Hardware - broad discussion on major research topics on neuromorphic hardware.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Neuromorphic silicon neuron circuits - Indiveri et al. (2013) - overview of building blocks for neuromorphic circuits.
  - Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware - Titirsha T and Das A (2020) - novel technique for mapping of SNNs to neuromorphic hardware using a thermal model.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - A Survey of Neuromorphic Computing and Neural Networks in Hardware - broad discussion on major research topics on neuromorphic hardware.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
  - Towards spike-based machine intelligence with neuromorphic computing - Roy et al. (2019) - overview of the main research direction in neuromorphic hardware and discussion of open questions and challenges in neuromorphic computing.
- Applied papers
  - A Brain-Inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle - using spiking neural networks for decision making for intelligent agents.
  - STDP-based spiking deep convolutional neural networks for object recognition - first paper proposing convolutional SNN architecture.
  - Spiking Neural Networks applied to the classification of motor tasks in EEG signals - using spiking networks for recognition of motor imagery tasks from EEG signals.
  - Combining SNN and ANN for enhanced image classification - combining SNN and ANN to get a hybrid model with improved performance for image classification.
  - One-shot learning with spiking neural networks - investigation of one-shot learning paradigm in spiking neural networks using local synaptic plasticity in RSNNs.
  - Visual Explanations from Spiking Neural Networks using Interspike Intervals - building biologically plausible Spike Activation Maps (SAM) for spike visualization.
  - STDP-based spiking deep convolutional neural networks for object recognition - first paper proposing convolutional SNN architecture.
  - Combining SNN and ANN for enhanced image classification - combining SNN and ANN to get a hybrid model with improved performance for image classification.
- Fundamental and overview papers
  - Networks of Spiking Neurons: The Third Generation of Neural Network Models - pioneering work on spiking neural networks.
  - On the computational power of circuits of spiking neurons - theoretical work, proving theorems about computational complexities of spiking networks.
  - Deep Learning With Spiking Neurons: Opportunities and Challenges - overview paper of deep learning on neuromorphic hardware using biologically plausible spiking neurons.
  - Deep learning in spiking neural networks - overview paper of advancements in deep learning for spiking neural networks.
  - Spiking Neural Networks and Online Learning: An Overview and Perspectives - overview paper of application of spiking neural networks in the online learning domain.
  - Recent Advances and New Frontiers in Spiking Neural Networks - state-of-the-art progress in network topology, neuromorphic datasets, neuromorphic hardware and optimization algorithms.
  - Deep learning in spiking neural networks - overview paper of advancements in deep learning for spiking neural networks.
  - Spiking Neural Networks and Online Learning: An Overview and Perspectives - overview paper of application of spiking neural networks in the online learning domain.
  - Recent Advances and New Frontiers in Spiking Neural Networks - state-of-the-art progress in network topology, neuromorphic datasets, neuromorphic hardware and optimization algorithms.
- ANN to SNN Conversion
  - Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing - defines algorithms for weight normalization for ann to snn conversion.
  - Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks - defines robust weight normalization and tools for converting different layers, like BatchNormalization, Maxpooling etc.
  - Conversion of continuous-valued deep networks to efficientevent-driven networks for image classification - spiking max-pooling and batch normalization.
  - Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation - hybrid ann to snn conversion.
  - Spiking Deep Residual Network - converting ResNet to a spiking version.
  - Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection - converting famous yolo architecture to the spiking version.
  - Optimal conversion of conventional artificial neural networks to spiking neural networks - more efficient approximation of loss function between ann and snn with weight transfer pipeline that combines threshold balance and soft-reset mechanisms.
  - Deep Residual Learning in Spiking Neural Networks - improved conversion of ResNet to a spiking version.
  - Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks - defines robust weight normalization and tools for converting different layers, like BatchNormalization, Maxpooling etc.
  - Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation - hybrid ann to snn conversion.
  - Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection - converting famous yolo architecture to the spiking version.
  - Spiking Deep Residual Network - converting ResNet to a spiking version.
  - Deep Residual Learning in Spiking Neural Networks - improved conversion of ResNet to a spiking version.
- Learning methods for SNNs
  - Spike timing dependent plasticity: a consequence of more fundamental learning rules - derivation of biological origin and plausibility of STDP.
  - A History of Spike-Timing-Dependent Plasticity - origins and history of STDP learning method.
  - Training Deep Spiking Neural Networks Using Backpropagation - treatment of membrane potentials as continuous signals and considering discontinuities as noise in backpropagation for SNN.
  - Event-driven random backpropagation: Enabling neuromorphic deep learning machines - random backpropagation as solution for problem of discrete backpropagation on spikes.
  - Surrogate gradient learning in spiking neural networks - surrogate method, which enables discrete backpropagation learning.
  - S4NN: temporal backpropagation for spiking neural networkswith one spike per neuron - backpropagation learning method, based on rank-order temporal coding.
  - Biologically inspired alternatives to backpropagation throughtime for learning in recurrent neural nets - biologically plausible approximation of backpropagation through time.
  - Surrogate gradient learning in spiking neural networks - surrogate method, which enables discrete backpropagation learning.
  - S4NN: temporal backpropagation for spiking neural networkswith one spike per neuron - backpropagation learning method, based on rank-order temporal coding.
  - Biologically inspired alternatives to backpropagation throughtime for learning in recurrent neural nets - biologically plausible approximation of backpropagation through time.
- Neuron models
  - A logical calculus of the ideas immanent in nervous activity - one of the first neuron models for computation, based on "all-or-none"-property of biological neurons.
  - A quantitative description of membrane current and its application to conduction and excitation in nerve - introduction of Hodgkin-Huxley neuron model.
  - Simple Model of Spiking Neurons - introduces the mathematical model of a new type of neurons, so called, Izhikevich neurons.
  - Resonate-and-fire neurons - resonate-and-fire model with complex state variable.
  - Which Model to Use for Cortical Spiking Neurons? - overview of computational efficiency and biological plausibility of different neuron models.
  - Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron - paper on leaky integrate-and-fire neuron model.
Books :closed_book:
- Neuronal Dynamics - introduction to theoretical and computational neuroscience.
- Neuronal Dynamics - Lectures - youtube playlists of lectures, based on the book "Neuronal Dynamics".
- Dynamical Systems in Neuroscience - theoretical neuroscience with exercises and solutions.
- Neuronal Dynamics - Lectures - youtube playlists of lectures, based on the book "Neuronal Dynamics".
Frameworks :computer:
- Neuromorphic hardware
  - NEST - spiking neural network simulator with focus on dynamics, size and structure of neural systems. Can be complemented by PyNN.
  - PySNN - framework for spiking neural netorks built on top of Pytorch.
  - PyNN - library for defining neural models independent of simulator specifics.
  - NengoDL - library for building, testing and deploying neural networks, especially spiking neural networks.
  - BindsNET - Python framework for simulation of spiking neural networks using Pytorch.
  - Brian2 - python simulator for spiking neural networks.
  - Norse - framework for spiking neural networks, which expands PyTorch with SNN primitives.
Others :memo:
- Neuromorphic hardware
  - Human Brain Project - european project for research in neuroscience, computing and brain-related medicine.
  - Spiking Neuron Simulation - tutorial on a simple spiking neuron simulation using Tensorflow.
  - LIF Simulation - tutorial on the leaky-integrate-and-fire simulation using Tensorflow.
  - Spiking Neuron Simulation - tutorial on a simple spiking neuron simulation using Tensorflow.
  - LIF Simulation - tutorial on the leaky-integrate-and-fire simulation using Tensorflow.
Repositories :open_file_folder:
- Neuromorphic hardware
  - snn-toolbox - toolbox for conversion of ANNs into SNNs using weight normalization.
  - spikeflow - library for spiking neural networks on top of Tensorflow.
  - hybrid-snn-conversion - hybrid ann to snn conversion with spike-based backpropagation.
  - SpikingJelly - new simple SNN framework in Pytorch with easy SNN initialization and ANN2SNN conversion.
  - BrainPy - simulation toolbox for computational neuroscience research.

Programming Languages

Python 6 Jupyter Notebook 1

Ecosyste.ms: Awesome

awesome-spiking-neural-networks

Papers :page_with_curl:

Neuromorphic hardware

Applied papers

Fundamental and overview papers

ANN to SNN Conversion

Learning methods for SNNs

Neuron models

Books :closed_book:

Frameworks :computer:

Neuromorphic hardware

Others :memo:

Neuromorphic hardware

Repositories :open_file_folder:

Neuromorphic hardware