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Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
- Publication Year :
- 2021
-
Abstract
- 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.<br />Comment: Accepted to International Conference on Neuromorphic Systems (ICONS 2021)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2106.11169
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1145/3477145.3477267