  This work introduces two novel neural spike detection schemes intended for
use in next-generation neuromorphic brain-machine interfaces (iBMIs). The
first, an Event-based Spike Detector (Ev-SPD) which examines the temporal
neighborhood of a neural event for spike detection, is designed for in-vivo
processing and offers high sensitivity and decent accuracy (94-97%). The
second, Neural Network-based Spike Detector (NN-SPD) which operates on hybrid
temporal event frames, provides an off-implant solution using shallow neural
networks with impressive detection accuracy (96-99%) and minimal false
detections. These methods are evaluated using a synthetic dataset with varying
noise levels and validated through comparison with ground truth data. The
results highlight their potential in next-gen neuromorphic iBMI systems and
emphasize the need to explore this direction further to understand their
resource-efficient and high-performance capabilities for practical iBMI
settings.
