Spiking neural networks for biomedical signal analysis

Area of Science:

• Neuroscience and Artificial Intelligence
• Biomedical Signal Processing

Background:

• Cloud-based AI models face challenges including high computational demands, privacy issues, communication delays, and energy consumption.
• On-device AI processing offers a solution by executing computations directly on devices, enhancing privacy, reducing latency, and improving power efficiency.
• Spiking neural networks (SNNs), inspired by the human brain, are emerging as a key technology for efficient on-device AI and real-time processing.

Purpose of the Study:

• To review the application of Spiking Neural Networks (SNNs) in the analysis of biomedical signals.
• To investigate the unique attributes and future potential of SNN models for biomedical signal analysis.

Main Methods:

• Literature review of Spiking Neural Networks (SNNs) applied to biomedical signal analysis.
• Analysis of SNNs' performance in processing electroencephalograms (EEGs), electrocardiograms (ECGs), and electromyograms (EMGs).
• Exploration of the distinctive attributes and future research directions for SNNs in this domain.

Main Results:

• SNNs demonstrate potential for efficient, low-power processing of biomedical signals on-device.
• The review highlights SNNs' capability to mimic brain efficiency for real-time biomedical data analysis.
• Distinctive attributes and future pathways for SNN models in biomedical signal analysis were identified.

Conclusions:

• Spiking neural networks (SNNs) represent a promising next generation of AI for on-device applications, particularly in biomedical signal analysis.
• SNNs offer a pathway to overcome limitations of traditional AI, providing efficient and private analysis of complex biological data.
• Further research into SNN models is crucial to fully realize their potential in advancing biomedical diagnostics and monitoring.