| Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks | 20 | 0.88 | 2019 |
| NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps. | 23 | 1.01 | 2019 |
| Surrogate Gradient Learning in Spiking Neural Networks. | 3 | 0.38 | 2019 |
| Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization. | 6 | 0.40 | 2019 |
| Small-footprint Spiking Neural Networks for Power-efficient Keyword Spotting | 1 | 0.35 | 2018 |
| A learning framework for winner-take-all networks with stochastic synapses. | 1 | 0.35 | 2018 |
| Synaptic Plasticity Dynamics for Deep Continuous Local Learning. | 3 | 0.38 | 2018 |
| Supervised learning based on temporal coding in spiking neural networks. | 21 | 0.66 | 2018 |
| Fast Classification Using Sparsely Active Spiking Networks | 3 | 0.36 | 2017 |
| Deep supervised learning using local errors. | 8 | 0.56 | 2017 |
| Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks. | 1 | 0.35 | 2017 |
| Stochastic Interpretation of Quasi-periodic Event-based Systems. | 0 | 0.34 | 2015 |
| Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States. | 3 | 0.57 | 2015 |
| An event-based architecture for solving constraint satisfaction problems | 6 | 0.55 | 2015 |
| Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits. | 1 | 0.37 | 2014 |
| Automated synthesis of asynchronous event-based interfaces for neuromorphic systems | 3 | 0.38 | 2013 |
| Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems. | 5 | 0.48 | 2013 |
