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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
Last synced: 4 days ago
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Papers :page_with_curl:
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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.
- 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.
- 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.
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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 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.
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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.
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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.
- Recent Advances and New Frontiers in Spiking Neural Networks - state-of-the-art progress in network topology, neuromorphic datasets, neuromorphic hardware and optimization algorithms.
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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.
- Conversion of continuous-valued deep networks to efficientevent-driven networks for image classification - spiking max-pooling and batch normalization.
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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.
- Event-driven random backpropagation: Enabling neuromorphic deep learning machines - random backpropagation as solution for problem of discrete backpropagation on spikes.
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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.
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Frameworks :computer:
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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.
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Others :memo:
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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.
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