Neuromorphic plasticity is the basic platform for learning in biological systems and is considered as the unique concept in the brains of vertebrates, which outperform today's most powerful digital computers when it comes to cognitive and recognition tasks. An emerging task in the field of neuromorphic engineering is to mimic neural pathways via elegant technological approaches to close the gap between biological and digital computing. In this respect, functional, memristive devices are considered promising candidates with yet unknown benefit for neuromorphic circuits. It is demonstrated that a single Pt/Ge0.3Se0.7/SiO2/Cu memristive device implemented in an analogue circuitry mimics non‐associative and associative types of learning. For Pavlovian conditioning, different threshold voltages for the memristive device and the comparator are essential. Similarities to neurobiological correlates of learning are discussed in the framework of hebbian learning rule, plasticity, and long‐term potentiation.

Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2−x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

Memristive devices help address the binding problem: Their memory supports a transient connectivity in oscillator networks.

The objective of this paper is to explore the possibility to couple two van der Pol (vdP) oscillators via a resistance-capacitance (RC) network comprising a Ag-TiO x -Al memristive device. The coupling was mediated by connecting the gate terminals of two programmable unijunction transistors (PUTs) through the network. In the high resistance state (HRS) the memresistance was in the order of M leading to two independent self-sustained oscillators characterized by the different frequencies f1 and f2 and no phase relation between the oscillations. After a few cycles and in dependency of the mediated pulse amplitude the memristive device switched to the low resistance state (LRS) and a frequency adaptation and phase locking was observed. The experimental results are underlined by theoretically considering a system of two coupled vdP equations. The presented neuromorphic circuitry conveys two essentials principle of interacting neuronal ensembles: synchronization and memory. The experiment may path the way to larger neuromorphic networks in which the coupling parameters can vary in time and strength and are realized by memristive devices.

SUMMARYA memristive device is a novel passive device, which is essentially a resistor with memory. This device can be utilized for novel technical applications like neuromorphic computation. In this paper, we focus on anticipation -a capability of a system to decide how to react in an environment by predicting future states. Especially, we have designed an elementary memristive circuit for the anticipation of digital patterns, where this circuit is based on the capability of an amoeba to anticipate periodically occurring unipolar pulses. The resulting circuit has been verified by digital simulations and has been realized in hardware as well. For the practical realization, we have used an Ag-doped TiO2−x-based memristive device, which has been fabricated in planar capacitor structures on a silicon wafer. The functionality of the circuit is shown by simulations and measurements. Finally, the anticipation of information is demonstrated by using images, where the robustness of this anticipatory circuit against noise and faulty intermediate information is visualized.