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S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition
In this work, we use CSNNs trained in an unsupervised manner with the Spike Timing-Dependent Plasticity (STDP) rule, and we introduce, for the first time, Spiking Separated Spatial and Temporal Convolutions (S3TCs) for the sake of reducing the number of parameters required for video analysis.
Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
Implementing this model with unsupervised STDP-based CSNNs allows us to further study the performance of these networks with video analysis.
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition
We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets.
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