# Deep Spiking Networks

arXiv: https://arxiv.org/abs/1602.08323 · HTML: https://mlsanity.com/paper/1602.08323/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2016.

## Built on by (most cited first, 7 of 7)

- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity](https://mlsanity.com/paper/2111.01760/) (2021)
- [SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks](https://mlsanity.com/paper/2302.00232/) (2023)
- [Navigating Local Minima in Quantized Spiking Neural Networks](https://mlsanity.com/paper/2202.07221/) (2022)
- [Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization](https://mlsanity.com/paper/2109.11045/) (2021)
- [Event-Stream Super Resolution using Sigma-Delta Neural Network](https://mlsanity.com/paper/2408.06968/) (2024)
- [Asynchronous Bioplausible Neuron for SNN for Event Vision](https://mlsanity.com/paper/2311.11853/) (2023)

## Abstract (arXiv)

We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only update their states when receiving signals from other neurons. Total computation of the network thus scales with the number of spikes caused by an input rather than network size. We show that the spiking Multi-Layer Perceptron behaves identically, during both prediction and training, to a conventional deep network of rectified-linear units, in the limiting case where we run the spiking network for a long time. We apply this architecture to a conventional classification problem (MNIST) and achieve performance very close to that of a conventional Multi-Layer Perceptron with the same architecture. Our network is a natural architecture for learning based on streaming event-based data, and is a stepping stone towards using spiking neural networks to learn efficiently on streaming data.