With the explosive growth of the demand for computing power and storage on chips, researchers have proposed an artificial synapse with biological synapse-like functions to achieve low-energy, high-efficiency parallel neuromorphic computation and perform various complex functions efficiently, in response to the shortcomings of “storage and computation separation” in traditional computing systems. In this paper, we fabricated an artificial synapse based on SrTi 0.99 Co 0.01 O 3 calcium titanite. It implements a variety of important synaptic learning and memory functions, including long-term and short-term plasticity, paired-pulse depression, spike-time-dependent plasticity. It has promising applications in the construction of artificial neural networks for processing massive data. Further studies show that the resistance switching ratio of SrTi 0.99 Co 0.01 O 3 is improved by an order of magnitude compared to SrTiO 3 , and the stability is greatly enhanced, which may be attributed to the increase in the content of oxygen vacancies in the material (from 20.3% to 23.9%), which is also more favorable to the synaptic performance. Therefore, Co has an important impact on the enhancement of SrTiO 3 properties. This study is a good reference for future full implementation of brain-inspired computing systems. • The device implements a variety of important synaptic learning and memory functions, including long-term and short-term plasticity, paired-pulse depression, spike-time-dependent plasticity. • we compared Sr(Ti,Co)O 3 with SrTiO 3 and found that the Co doping gives the device a larger switching ratio and better stability. • The introduction of Co increases oxygen vacancies from 20.3% to 23.9%, promoting device performance.