SpikingConvNet

Implementation of the paper STDP-based spiking deep neural networks for object recognition, available here, for the MNIST classification task.

Results

The model achieves ~95% accuracy, better performance can be reached with more tuning.

MNIST Dataset

See https://pypi.org/project/python-mnist/ to download the dataset.

References:

[1] Kheradpisheh, S. R., Ganjtabesh, M., Thorpe, S. J., & Masquelier, T. (2018). STDP-based spiking deep convolutional neural networks for object recognition. Neural Networks, 99, 56–67. https://doi.org/10.1016/J.NEUNET.2017.12.005

[2] Mozafari, M., Ganjtabesh, M., Nowzari-Dalini, A., & Masquelier, T. (2019). SpykeTorch: Efficient simulation of convolutional spiking neural networks with at most one spike per neuron. Frontiers in Neuroscience, 13, 625. https://doi.org/10.3389/FNINS.2019.00625

[3] https://github.com/npvoid/SDNN_python