NEUROMORPHIC-ENABLED VIDEO-ACTIVATED CELL SORTING FOR HIGH-ACCURACY CLASSIFICATION OF REGULAR RED BLOOD CELLS AND BLOOD-DISEASE-RELATED SPHEROCYTES

The image-activated cell sorter (IACS), serving as a comprehensive setup of imaging flow cytometry (IFC), holds extensive utility for cellular heterogeneity investigation [1].However, current IACS frameworks encounter challenges like 3D information loss and processing latency during real-time sorting.Previously [2], we established the neuromorphic-enabled video-activated cell sorter (NEVACS) in a reducing-redundancy-for-efficiency strategy and applied in classifying Hela cells and beads.Herein, we further implemented NEVACS by using spiking neural network (SNN) models and deployed them on a brain-inspiring neuromorphic chip to replace the previous implementation of GPU.With this new implementation, we applied NEVACS in the application of accurately sorting regular red blood cells (RBCs) and blood-disease-relevant spherocytes, highlighting the NEVACS' superior accuracy over IACS.