Neuromorphic Datasets

Neuromorphic datasets are time-series datasets representing asynchronous events, typically from event cameras or other neuromorphic sensors, used to train and evaluate spiking neural networks (SNNs). Current research focuses on improving SNN accuracy and efficiency on these datasets, exploring architectures like Spiking Transformers and employing techniques such as knowledge distillation, adaptive threshold learning, and novel training algorithms (e.g., event-driven learning, zeroth-order methods) to overcome challenges posed by the unique characteristics of neuromorphic data. This research is significant because it advances the development of energy-efficient, biologically-inspired AI systems with potential applications in low-power devices and robotics, while also raising important considerations regarding data privacy and security in these systems.