The field of neuromorphic computing and event-based vision is rapidly evolving, with a focus on developing efficient and low-power systems for applications such as brain-machine interfaces and object detection. Recent research has explored the use of hybrid neural decoders, spiking neural networks, and event-based vision transformers to improve performance and reduce computational demands. Noteworthy papers include:

• Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI, which proposes a streamlined decoding pipeline for neuromorphic implantable brain-machine interfaces.
• Hybrid Spiking Vision Transformer for Object Detection with Event Cameras, which introduces a novel hybrid spike vision transformer model for event-based object detection tasks.