Neuromorphic Analog Implementation of Reservoir Computing for Machine Learning

In reservoir computing, dynamical systems are used to drive state-of-the-art machine learning with small training sets and minimal computing resources. Neuromorphic (brain-inspired) computing pose to further improve reservoir computing with energy-efficient spiking neural implementations. Here we propose an analog circuit design for reservoir computing using OZ spiking neurons, STDP (Spike-timing-dependent plasticity) synapses, and learning PES (prescribed error sensitivity) circuitry. We evaluated our design on a small scale using the Iris flower data set, demonstrating the potential application of neuromorphic analog hardware in reservoir computing.