Toward Hardware Spiking Neural Networks with Mixed-Signal Event-Based Learning Rules

Hardware spiking neural networks that co-integrate analog silicon neurons with memristive synaptic crossbar arrays are promising candidates to achieve low-power processing of event-based data. Learning patterns with real-world timescales, often exceeding the millisecond range, is however difficult with fully analog systems. In this work, we propose to overcome this challenge by introducing mixed-signal strategies to implement hardware-friendly learning rules derived from Spike Timing-Dependent Plasticity. By system-level simulation means, we illustrate the potential of this concept for both unsupervised and reward-modulated learning. In particular, we investigate how such learning rules and their tuning impact the overall system recognition rate depending on different characteristics of event-based inputs or synapses. This work provides useful insights for building versatile energy-efficient event-based neuromorphic systems with online learning capability.