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Found 2 papers, 1 papers with code
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations.
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.
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