Resistivity, Hall effect, and magnetoresistance are reported on a large set of semiconducting SrTiO 3−␦ single crystals doped n-type ͑by reduction or Nb substitution͒ over a broad range of carrier density ͑the 10 15 to mid 10 20 cm −3 range͒. Temperature-independent carrier densities, strongly temperature-dependent mobilities ͑up to 22 000 cm 2 V −1 s −1 at 4.2 K͒, and a remarkably low critical carrier density for the metal-insulator transition are observed, and interpreted in terms of the known quantum paraelectricity of the host. We argue that an unusual, high mobility, low density, metallic state is thus established at carrier densities at least as low as 8.5ϫ 10 15 cm −3 , in contrast to some prior conclusions. At low temperatures, the temperature dependence of the mobility and resistivity exhibit a nonmonotonic carrier density dependence and an abrupt change in character near 2 ϫ 10 16 cm −3 , indicating a distinct crossover in conduction mechanism, perhaps associated with a transition from impurity-band to conduction-band transport. The results provide a simple framework for the understanding of the global transport behavior of doped SrTiO 3 . Finally, it is proposed that the large residual resistivity ratios ͑Ͼ3000͒, and large, temperature independent, Hall coefficients ͑Ͼ1700 cm 3 C −1 ͒, demonstrate considerable potential for high-sensitivity resistive thermometry and Hall sensing applications.

Resistive switching devices were used as technological synapses to learn about the spatial- and temporal-correlated neuron spikes.

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~104) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

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