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Neuromorphic hardware equipped with learning capabilities can adapt to new,real-time data. While models of Spiking Neural Networks (SNNs) can now betrained using gradient descent to reach an accuracy comparable to equivalentconventional neural networks, such learning often relies on external labels.However, real-world data is unlabeled which can make supervised methodsinapplicable. To solve this problem, we propose a Hybrid Guided VariationalAutoencoder (VAE) which encodes event based data sensed by a Dynamic VisionSensor (DVS) into a latent space representation using an SNN. Theserepresentations can be used as an embedding to measure data similarity andpredict labels in real-world data. We show that the Hybrid Guided-VAE achieves87% classification accuracy on the DVSGesture dataset and it can encode thesparse, noisy inputs into an interpretable latent space representation,visualized through T-SNE plots. We also implement the encoder component of themodel on neuromorphic hardware and discuss the potential for our algorithm toenable real-time learning from real-world event data.

Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardware