Neuromorphic imaging flow cytometry combined with adaptive recurrent spiking neural networks

We present an experimental imaging flow cytometer using a 1 µs temporal resolution event-based complementary metal-oxide semiconductor (CMOS) camera, with data processed by adaptive feedforward and recurrent spiking neural networks. Our study classifies polymethyl methacrylate (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 long short-term memory (LSTM) and gated recurrent unit (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.