Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction

27 Mar 2020Ali SamadzadehFatemeh Sadat Tabatabaei FarAli JavadiAhmad NickabadiMorteza Haghir Chehreghani

Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to conventional artificial neural networks (ANNs), while preserving ANN's properties... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Event data classification CIFAR10-DVS DeepResidualSNN Accuracy 68.3 # 1
Image Classification MNIST ConvSNN Percentage error 0.6 # 16

Methods used in the Paper


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