# Deep Convolutional Spiking Neural Networks for Image Classification Source: [Deep Convolutional Spiking Neural Networks for Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:50:28.993Z Authors: Ruthvik Vaila, John Chiasson, Vishal Saxena Published: 2019-03-28T21:15:08.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()