Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale crossbar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a newgeneration robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

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nanoelectronic devices with the effect of resistive switching, which consists in a reversible change of resistance in response to electrical stimulation [5] and is identified with the memristive effect. [6] Despite the significant progress in understanding of the memristive effect and approaching maturity of the technology of resistiveswitching devices over the last 10 years, there are still a number of fundamental problems to solve.A key problem on the way of using resistive-switching devices as programmable elements in memory devices and mixed analog-digital processors of new generation is the variability of resistive switching parameters inherent to memristive thin-film devices. [7] Achieving stable switching between the nonlinear resistive states is also an important task on the way to implementing large passive crossbar arrays of memristors and solving the problem of leakage currents in them. [8,9] Metal-oxide memristive devices are most compatible with the traditional complementary metal oxide semiconductor (CMOS) process and exhibit a valence change memory effect. [10] The variation of switching parameters in such devices is caused by the stochastic nature of migration of oxygen ions and/or vacancies responsible for the local oxidation and recovery of conductive channels (filaments) and is accompanied by the degradation of switching parameters in the case of uncontrolled oxygen exchange between the dielectric and electrode materials.The traditional approaches to control the reproducibility of resistive switching include the formation of special electric field concentrators [11][12][13] and appropriate selection of materials/interfaces in memristive device structure. In the latter case, bilayer or multilayer structures are formed, in which the switching oxide alternates with a barrier/buffer layer (layers) to control the migration of oxygen vacancies, [14,15] with a layer of low dielectric constant [16,17] to obtain nonlinear currentvoltage (I-V) characteristics, or with a layer of higher/lower thermal conductivity [18,19] for the removal/retention of heat in the switching area and to achieve analog switching character. To tune the resistive states with given accuracy, regardless of

The breakthrough in electronics and information technology is anticipated by the development of emerging memory and logic devices, artificial neural networks and brain-inspired systems on the basis of memristive nano-materials represented, in a particular case, by a simple 'metal-insulator-metal' (MIM) thin-film structure. The present article is focused on the comparative analysis of MIM devices based on oxides with dominating ionic (ZrOx, HfOx) and covalent (SiOx, GeOx) bonding of various composition and geometry deposited by magnetron sputtering. The studied memristive devices demonstrate reproducible change in their resistance (resistive switching - RS) originated from the formation and rupture of conductive pathways (filaments) in oxide films due to the electric-field-driven migration of oxygen vacancies and/or mobile oxygen ions. It is shown that, for both ionic and covalent oxides under study, the RS behaviour depends only weakly on the oxide film composition and thickness, device geometry (down to a device size of about 20x20 mu m(2)). The devices under study are found to be tolerant to ion irradiation that reproduces the effect of extreme fluences of high-energy protons and fast neutrons. This common behaviour of RS is explained by the localized nature of the redox processes in a nanoscale switching oxide volume. Adaptive (synaptic) change of resistive states of memristive devices is demonstrated under the action of single or repeated electrical pulses, as well as in a simple model of coupled (synchronized) neuron-like generators. It is concluded that the noise-induced phenomena cannot be neglected in the consideration of a memristive device as a nonlinear system. The dynamic response of a memristive device to periodic signals of complex waveform can be predicted and tailored from the viewpoint of stochastic resonance concept. (C) 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.