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ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
All-Optical Excitable Spiking Laser Neuron in InP Generic...
Lukas Puts, Daan Lenstra, Kevin A. Williams, Weiming Yao · 2024-11-01 · via eess.SP updates on arXiv.org

Brain-inspired, neuromorphic devices implemented in integrated photonic hardware have attracted significant interest recently as part of efforts towards novel non-von Neumann computing paradigms that make use of the low loss, high-speed and parallel operations in optics. An all-optical spiking laser neuron fabricated on the indium-phosphide generic integration technology platform may be a practical alternative to other semi-integrated photonic and electronic-based spiking neuron implementations. Owing to the large number of predefined building blocks, a plethora of applications have benefitted already from the generic integration process. This technology platform has now been utilised for the first time to demonstrate an all-optical spiking laser neuron. This paper present and discusses the design and measurement of the ultra-fast and rich spiking dynamics in these devices. We show that under external pulse injection and operated slightly below the lasing threshold, the laser neuron exhibits an excitable mode, in addition to a self-spiking mode far above the threshold when no pulse is injected. In the excitable mode, the required injected pulse energy is much lower than that of the generated excited response, meeting an important requirement for neuron cascadability. In addition, we investigate excitability at different injection wavelengths below the lasing wavelength, as well as the ultra-fast temporal properties of the spiking response. All of the discussed characteristics point to the laser neuron being an important candidate for scaling up to future fully-connected, multi-wavelength all-optical photonic spiking neural networks in indium-phosphide generic integration technology.