  Neuromorphic computing based on spiking neural networks has the potential to
significantly improve on-line learning capabilities and energy efficiency of
artificial intelligence, specially for edge computing. Recent progress in
computational neuroscience have demonstrated the importance of heterosynaptic
plasticity for network activity regulation and memorization. Implementing
heterosynaptic plasticity in hardware is thus highly desirable, but important
materials and engineering challenges remain, calling for breakthroughs in
neuromorphic devices. In this mini-review, we propose an overview of the latest
advances in multi-terminal memristive devices on silicon with tunable synaptic
plasticity, enabling heterosynaptic plasticity in hardware. The scalability and
compatibility of the devices with industrial complementary metal oxide
semiconductor (CMOS) technologies are discussed.
