Spiking neural networks with resistive-switching synapses for STDP-based unsupervised learning

Emulating the architecture and learning mode of the human brain is a grand challenge for the modern neuromorphic engineering. Toward this goal, neural networks should feature spiking neurons and plastic synapses capable of high connection density and spike timing dependent plasticity (STDP) for unsupervised learning of external stimuli. This work illustrates the state of the art of spiking neural networks (SNNs) with nanoscale synapses based on resistive switching memory (RRAM) devices. The RRAM device engineering, the synapse circuit design and plasticity, and the hardware demonstration of unsupervised learning in the SNN will be described. The challenges for high-performance SNNs, including the materials engineering for high RRAM reliability, the synapse architecture to enable STDP, the spiking distribution to support unsupervised learning, and the hybrid integration of RRAM synapses and silicon-based neurons on the same chip will be reviewed. Finally, novel research paths for brain- inspired recurrent SNNs featuring associative memory, pattern recognition and error correction will be discussed.