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arXiv · 2505.11433

Neuromorphic Imaging Flow Cytometry combined with Adaptive Recurrent Spiking Neural Networks

Georgios Moustakas·Ioannis Tsilikas·Adonis Bogris·Charis Mesaritakis
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Abstract

We present an experimental imaging flow cytometer using a 1 {\mu}s 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 {\mu}m) 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.

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Georgios Moustakas, Ioannis Tsilikas, Adonis Bogris, Charis Mesaritakis. 2025-05-16. Neuromorphic Imaging Flow Cytometry combined with Adaptive Recurrent Spiking Neural Networks. https://arxiv.org/abs/2505.11433

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