  The rapid development of brain-inspired computing requires new artificial
components and architectures for its hardware implementation. In this regard,
memristive devices emerged as potential candidates for artificial synapses
because of their ability to emulate the plasticity of the biological synapses.
In this work, the synaptic behavior of the TiN/La2NiO4+{\delta}/Pt memristive
devices based on thermally annealed La2NiO4+{\delta} films is thoroughly
investigated. Using electron energy loss spectroscopy, we show that annealing
using reducing (Ar) or oxidizing (O2) atmospheres affects the interstitial
oxygen content ({\delta}) in the La2NiO4+{\delta} films. Electrical
characterization shows that both devices exhibit long-term
potentiation/depression and spike-timing-dependent plasticity, which makes them
suitable for neuromorphic applications. At the same time, the Ar annealed
TiN/La2NiO4+{\delta}/Pt device demonstrates non-volatile properties with low
energy consumption during the learning process. On the other hand, in the O2
annealed TiN/La2NiO4+{\delta}/Pt device the resistive switching behavior is
more volatile and requires more energy for synaptic learning. Finally, the
simulation tools show that spiking neural network architectures with
unsupervised learning rules based on the experimental data achieve high
inference accuracy in the digit recognition task, which proves the potential of
TiN/La2NiO4+{\delta}/Pt devices for artificial synapse applications.
