Efficient-Scalable-Machine-Learning/event-ssm
Official implementation of our paper "Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models"
This project helps researchers and engineers working with neuromorphic sensors to understand complex, rapidly changing data streams. It takes raw event data from sensors that mimic biological nervous systems and classifies it, for example, identifying spoken words or gestures. This tool is designed for specialists in neuromorphic computing, computational neuroscience, and event-based AI applications.
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Use this if you need to process and classify long, irregularly sampled, high-volume data from asynchronous event-based sensors, especially in neuromorphic vision or audio.
Not ideal if your data comes from traditional frame-based cameras or regularly sampled time-series sensors, as this tool is specifically optimized for event-based signals.
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23
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4
Language
Python
License
MIT
Category
Last pushed
Oct 02, 2025
Commits (30d)
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