  Metal-oxide memristors have emerged as promising candidates for hardware
implementation of artificial synapses - the key components of high-performance,
analog neuromorphic networks - due to their excellent scaling prospects. Since
some advanced cognitive tasks require spiking neuromorphic networks, which
explicitly model individual neural pulses (spikes) in biological neural
systems, it is crucial for memristive synapses to support the
spike-time-dependent plasticity (STDP), which is believed to be the primary
mechanism of Hebbian adaptation. A major challenge for the STDP implementation
is that, in contrast to some simplistic models of the plasticity, the
elementary change of a synaptic weight in an artificial hardware synapse
depends not only on the pre-synaptic and post-synaptic signals, but also on the
initial weight (memristor's conductance) value. Here we experimentally
demonstrate, for the first time, STDP protocols that ensure self-adaptation of
the average memristor conductance, making the plasticity stable, i.e.
insensitive to the initial state of the devices. The experiments have been
carried out with 200-nm Al2O3/TiO2-x memristors integrated into 12x12
crossbars. The experimentally observed self-adaptive STDP behavior has been
complemented with numerical modeling of weight dynamics in a simple system with
a leaky-integrate-and-fire neuron with a random spike-train input, using a
compact model of memristor plasticity, fitted for quantitatively correct
description of our memristors.
