Temporal spiking Hebbian learning rule: a photonic implementation in biological neural systems

This paper introduces a photonic architecture that emulates Hebbian learning in biological neural systems. By integrating micro-ring filters, phase change material-based micro-ring modulators, and photodetectors, the system simulates neuronal components like synapses and dendrites. Each Photonic Synapse Block (PSB) operates at distinct wavelengths, allowing for multiple synaptic inputs through serial connections. A microcontroller dynamically adjusts synaptic weights based on neuronal spike patterns, enabling high-speed studies of synaptic dynamics and neural plasticity. The inclusion of PCM enhances the system’s adaptive learning capabilities, crucial for understanding neural behaviors and disorders. This architecture not only accelerates research in neuromorphic photonics but also demonstrates scalability and practicality through an active photonic integrated circuit developed with AIM Photonics. This work represents a significant step toward bridging the gap between theoretical neuromorphic models and their practical applications.