EXODUS: Stable and Efficient Training of Spiking Neural Networks

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Supervised learning based on temporal coding in spiking neural networks

Hesham Mostafa · 2017

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Assessing the scalability of biologically-motivated deep learning algorithms and architectures

Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018

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Gradient descent for spiking neural networks

Dongsung Huh and Terrence J Sejnowski · 2018

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Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning

Chankyu Lee, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy · 2018

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The heidelberg spiking data sets for the systematic evaluation of spiking neural networks

Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, and Friedemann Zenke · 2020

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Training deep spiking neural networks

Original

Eimantas Ledinauskas, Julius Ruseckas, Alfonsas Juršėnas, and Giedrius Buračas · 2020

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Spatial properties of stdp in a self-learning spiking neural network enable controlling a mobile robot

Sergey A. Lobov, Alexey N. Mikhaylov, Maxim Shamshin, Valeri A. Makarov, and Victor B. Kazantsev · 2020

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Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization

Priyadarshini Panda, Aparna Aketi, and Kaushik Roy · 2020

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Deep learning incorporating biologically inspired neural dynamics and in-memory computing

Stanisław Woźniak, Angeliki Pantazi, Thomas Bohnstingl, and Evangelos Eleftheriou · 2020