Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors

Abstract

Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.