1 research outputs found
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Surface electromyogram (sEMG) signals result from muscle movement and hence
they are an ideal candidate for benchmarking event-driven sensing and
computing. We propose a simple yet novel approach for optimizing the spike
encoding algorithm's hyper-parameters inspired by the readout layer concept in
reservoir computing. Using a simple machine learning algorithm after spike
encoding, we report performance higher than the state-of-the-art spiking neural
networks on two open-source datasets for hand gesture recognition. The spike
encoded data is processed through a spiking reservoir with a biologically
inspired topology and neuron model. When trained with the unsupervised activity
regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir
yields better performance than state-of-the-art convolutional neural networks.
The reservoir performance with regulated activity was found to be 89.72% for
the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset.
Therefore, the biologically-inspired computing paradigm, which is known for
being power efficient, also proves to have a great potential when compared with
conventional AI algorithms.Comment: Accepted to International Conference on Neuromorphic Systems (ICONS
2021