NEUROMORPHIC IMAGING FLOW CYTOMETRY COMBINED WITH ADAPTIVE RECURRENT SPIKING NEURAL NETWORKS
UNIVERSITY OF WEST ATTICA & SCHOOL OF APPLIED MATHEMATICAL
AND PHYSICAL SCIENCES
Georgios Moustakas, Ioannis Tsilikas, Adonis Bogris, Charis Mesaritakis
ABSTRACT
We present an experimental imaging flow cytometer using a 1 μs temporal resolution event-based CMOS camera, with data processed by adaptive feedforward and recurrent spiking neural networks. Our study classifies PMMA particles (12, 16, 20 μm) flowing at 0.7 m/s in a microfluidic channel. Processing of experimental data highlighted that spiking recurrent networks, including LSTM and GRU models, achieved 98.4% accuracy by leveraging temporal dependencies. Additionally, adaptation mechanisms in lightweight feedforward spiking networks improved accuracy by 4.3%. This work outlines a technological roadmap for neuromorphic-assisted biomedical applications, enhancing classification performance while maintaining low latency and sparsity.
