# Deep Convolutional Spiking Neural Networks for Image Classification

arXiv: https://arxiv.org/abs/1903.12272 · HTML: https://mlsanity.com/paper/1903.12272/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Bayesian Continual Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.13723/) (2022)
- [An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks](https://mlsanity.com/paper/2110.10217/) (2021)
- [Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning](https://mlsanity.com/paper/2301.07275/) (2023)
- [A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks](https://mlsanity.com/paper/2210.00894/) (2022)

## Abstract (arXiv)

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real life applications. In this work we focus on implementing a spiking CNN using Tensorflow to examine behaviour of the network and empirically study the effect of various parameters on learning capabilities and also study catastrophic forgetting in the spiking CNN and weight initialization problem in R-STDP using MNIST and N-MNIST data sets.