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SPARKS 🎆

This is the official repository for SPARKS: a Sequential Predictive Autoencoder for the Analysis of spiKing Signals.

SPARKS includes a novel self-attention mechanism using Hebbian learning to generate reliable latent representations from single spike timings. SPARKS trains a variational autoencoder with a novel criterion inspired by predictive coding for temporal coherence.

sparks is implemented in PyTorch and includes demos for a quickstart. It can perform supervised or unsupervised to produce low-dimensional latent embeddings which allows to gain biological insights from neural data.

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Reference

• 📄 Preprint:

Available now on BiorXiv!

License

• SPARKS is an open source software under a GLPv3 license.