Memristive nano-neuromorphic spiking neural network with self-adaptive continual and predictive learning for edge gesture recognition

Gesture recognition refers to a surface electromyography based method of gesture recognition becoming an essential element of edge computing systems to aid human-machine interface, wearable interfaces, and assistive technology systems. The latest advancements in deep learning have shown encouraging accuracy and are still constrained by frame-based processing, offline learning, excessive energy usage, and the incapacity to adjust to the constantly changing patterns of gestures. These limitations limit the use in edge devices with limited resources. To address them, SCaP-SNN (Self-adaptive Continuous and Predictive Spiking Neural Network) is presented as a memristive nano-neuromorphic model of adaptive and power efficient gesture recognition in the edge. Self-adaptive learning is described as dynamically modulating synaptic updates according to the variance of the signal, continual learning is used to retain previous knowledge about gestures while receiving incremental updates, and predictive learning can predict future gestures for a short period of time based on temporal spike dependence. The main innovation of this study is the design of a unified memristive spiking neural network incorporating self-adaptive learning, continual learning, and predictive intelligence for efficient gesture recognition at the edge of the network. The presented approach combines the spiking neural networks with memristive synapses modeling in order to support event-based computation, self-adaptive plasticity, lifelong learning without disastrous forgetting, and predictive inference. The online synaptic adaptation and consolidation processes allow to stabilize learning of new gestures without loss of the earlier learnt information The experimental results show a classification accuracy of 96.8%, F1-score of 0.965, low forgetting rate of 2.1% and a prediction accuracy of 91.4%. Power consumption analysis shows that the mean power consumption is 28 microjoules per inference, which is suitable in being used in low-power edges.