README.md

This repository is created to implement classification of MNIST dataset using SNN

Following papers have been used as a reference:-

1.STDP-based Unsupervised Feature Learning using Convolution-over-time in Spiking Neural Networks for Energy-Efficient Neuromorphic Computing by Gopalakrishnan Srinivasan, Priyadarshini Panda and Kaushik Roy

2.Unsupervised learning of digit recognition using spike-timing-dependent plasticity by Peter U. Diehl and Matthew Cook Institute

Observations so far:-

1. Basic Network

Number of input neurons : 784 (image 28x28 -> 784 x 1 vector)
Number of hidden neurons : 0
Number of output neurons : 800 neurons trained on 80 samples of each digit
Classification accuracy on MNIST Test set = 71.49%