  It is now widely accepted that memristive devices are perfect candidates for
the emulation of biological synapses in neuromorphic systems. This is mainly
because of the fact that like the strength of synapse, memristance of the
memristive device can be tuned actively (e.g., by the application of volt- age
or current). In addition, it is also possible to fabricate very high density of
memristive devices (comparable to the number of synapses in real biological
system) through the nano-crossbar structures. However, in this paper we will
show that there are some problems associated with memristive synapses
(memristive devices which are playing the role of biological synapses). For
example, we show that the variation rate of the memristance of memristive
device depends completely on the current memristance of the device and
therefore it can change significantly with time during the learning phase. This
phenomenon can degrade the performance of learning methods like Spike
Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic
systems to become unstable. Finally, at the end of this paper, we illustrate
that using two serially connected memristive devices with different polarities
as a synapse can somewhat fix the aforementioned problem.
