Issues in the circuitry, integration, and material properties of the two-dimensional (2D) and three-dimensional (3D) crossbar array (CBA)-type resistance switching memories are described. Two important quantitative guidelines for the memory integration are provided with respect to the required numbers of signal wires and sneak current paths. The advantage of 3D CBAs over 2D CBAs (i.e., the decrease in effect memory cell size) can be exploited only under certain limited conditions due to the increased area and layout complexity of the periphery circuits. The sneak current problem can be mitigated by the adoption of different voltage application schemes and various selection devices. These have critical correlations, however, and depend on the involved types of resistance switching memory. The problem is quantitatively dealt with using the generalized equation for the overall resistance of the parasitic current paths. Atomic layer deposition is discussed in detail as the most feasible fabrication process of 3D CBAs because it can provide the device with the necessary conformality and atomic-level accuracy in thickness control. Other subsidiary issues related to the line resistance, maximum available current, and fabrication technologies are also reviewed. Finally, a summary and outlook on various other applications of 3D CBAs are provided.

We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics – determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability – due to the variability of the threshold switching behavior – of individual neurons.

Chemical synapses are important components of the large-scaled neural network in the hippocampus of the mammalian brain, and a change in their weight is thought to be in charge of learning and memory. Thus, the realization of artificial chemical synapses is of crucial importance in achieving artificial neural networks emulating the brain's functionalities to some extent. This kind of research is often referred to as neuromorphic engineering. In this study, we report short-term memory behaviours of electrochemical capacitors (ECs) utilizing TiO2 mixed ionic-electronic conductor and various reactive electrode materials e.g. Ti, Ni, and Cr. By experiments, it turned out that the potentiation behaviours did not represent unlimited growth of synaptic weight. Instead, the behaviours exhibited limited synaptic weight growth that can be understood by means of an empirical equation similar to the Bienenstock-Cooper-Munro rule, employing a sliding threshold. The observed potentiation behaviours were analysed using the empirical equation and the differences between the different ECs were parameterized.