# Training Spiking Deep Networks for Neuromorphic Hardware

arXiv: https://arxiv.org/abs/1611.05141 · HTML: https://mlsanity.com/paper/1611.05141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2016.

## Built on by (most cited first, 12 of 12)

- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [Spiking Neural Networks for Frame-based and Event-based Single Object Localization](https://mlsanity.com/paper/2206.06506/) (2022)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks](https://mlsanity.com/paper/2207.04889/) (2022)
- [SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2312.17216/) (2023)
- [Direct Training via Backpropagation for Ultra-low Latency Spiking Neural Networks with Multi-threshold](https://mlsanity.com/paper/2112.07426/) (2021)
- [Foveal-pit inspired filtering of DVS spike response](https://mlsanity.com/paper/2105.14331/) (2021)
- [Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization](https://mlsanity.com/paper/2109.11045/) (2021)
- [Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection](https://mlsanity.com/paper/2307.08501/) (2023)
- [Implementing a foveal-pit inspired filter in a Spiking Convolutional Neural Network: a preliminary study](https://mlsanity.com/paper/2105.14326/) (2021)
- [Spike-based Neuromorphic Computing for Next-Generation Computer Vision](https://mlsanity.com/paper/2310.09692/) (2023)
- [Memory Networks: Towards Fully Biologically Plausible Learning](https://mlsanity.com/paper/2409.17282/) (2024)

## Abstract (arXiv)

We describe a method to train spiking deep networks that can be run using leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012 benchmark. Our method for transforming deep artificial neural networks into spiking networks is scalable and works with a wide range of neural nonlinearities. We achieve these results by softening the neural response function, such that its derivative remains bounded, and by training the network with noise to provide robustness against the variability introduced by spikes. Our analysis shows that implementations of these networks on neuromorphic hardware will be many times more power-efficient than the equivalent non-spiking networks on traditional hardware.