Work

Neuromorphic imaging flow cytometry combined with adaptive recurrent spiking neural networks

Year: 2025

Type: article

Abstract: 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.

Source: Optics Express

DOI: 10.1364/oe.564050

Authors: Georgios Moustakas, Ioannis Tsilikas, Adonis Bogris, Charis Mesaritakis

Institution: University of the Aegean

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: HORIZON EUROPE Digital, Industry and Space