  In the mammalian nervous system, various synaptic plasticity rules act,
either individually or synergistically, and over wide-ranging timescales to
dictate the processes that enable learning and memory formation. To mimic
biological cognition for artificial intelligence, neuromorphic computing
platforms thus call for synthetic synapses, that can faithfully express such
complex plasticity and dynamics. Although some plasticity rules have been
emulated with elaborate CMOS and memristive circuitry, hardware demonstrations
that combine multiple plasticities, such as long-term (LTP) and short-term
plasticity (STP) with tunable dynamics and within the same low-power nanoscale
devices have been missing. Here, we introduce phase change memtransistive
synapse that leverages the non-volatility of memristors and the volatility of
transistors for coupling LTP with homo and heterosynaptic STP effects. We show
that such biomimetic synapses can enable some powerful cognitive frameworks,
such as the short-term spike-timing-dependent plasticity (ST-STDP) and
stochastic Hopfield neural networks. We demonstrate how, much like the
mammalian brain, such emulations can establish temporal relationships in data
streams for the task of sequential learning and combinatorial optimization.
