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 cross-bar 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 new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

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Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.

The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driving signal is analyzed through the time series statistics of the resistive switching parameters, the spectral response to a periodic perturbation and the signal-to-noise ratio at the output of the nonlinear system The stabilized resistive switching and the increased memristance response are revealed in the observed regularities at an optimal noise intensity corresponding to the stochastic resonance phenomenon and interpreted using a stochastic memristor model taking into account an external noise source added to the control voltage The obtained results clearly show that noise and fluctuations can play a constructive role in nonlinear memristive systems far from equilibrium

Stochastic resonance in a metal-oxide memristive device

The low-frequency noise in a nanometer-sized virtual memristor consisting of a contact of a conductive atomic force microscope (CAFM) probe to an yttria stabilized zirconia (YSZ) thin film deposited on a conductive substrate is investigated. YSZ is a promising material for the memristor application since it is featured by high oxygen ion mobility, and the oxygen vacancy concentration in YSZ can be controlled by varying the molar fraction of the stabilizing yttrium oxide. Due to the low diameter of the CAFM probe contact to the YSZ film (∼10 nm), we are able to measure the electric current flowing through an individual filament both in the low resistive state (LRS) and in the high resistive state (HRS) of the memristor. Probability density functions (Pdfs) and spectra of the CAFM probe current in both LRS and HRS are measured. The noise in the HRS is found to be featured by nearly the same Pdf and spectrum as the inner noise of the experimental setup. In the LRS, a flicker noise 1/fγ with γ ≈ 1.3 is observed in the low-frequency band (up to 8 kHz), which is attributed to the motion (drift/diffusion) of oxygen ions via oxygen vacancies in the filament. Activation energies of oxygen ion motion determined from the flicker noise spectra are distributed in the range of [0.52; 0.68] eV at 300 K. Knowing these values is of key importance for understanding the mechanisms of the resistive switching in YSZ based memristors as well as for the numerical simulations of memristor devices.

Measurement of the activation energies of oxygen ion diffusion in yttria stabilized zirconia by flicker noise spectroscopy

In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 104), retention (≥104 s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.

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Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications

We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory, on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements.

Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

Multilayer Metal-Oxide Memristive Device with Stabilized Resistive Switching

Design and training principles have been proposed and tested for an artificial neural network based on metal-oxide thin-film nanostructures possessing bipolar resistive switching (memristive) effect. Experimental electronic circuit of neural network is implemented as a double-layer perceptron with a weight matrix composed of 32 memristive devices. The network training algorithm takes into account technological variations of the parameters of memristive devices. Despite the limited size of weight matrix the developed neural network model is scalable and capable of solving nonlinear classification problems. The learning and functionality of the network are demonstrated by using its computer model for the classification of activity propagation directions in simulated neuronal culture.

One-Board Design and Simulation of Double-Layer Perceptron Based on Metal-Oxide Memristive Nanostructures

An approach to formation and training of an artificial neural network (ANN) based on thin-film memristive metal–oxide–metal nanostructures, which exhibit the effect of bipolar resistive switching, has been proposed. An experimental electric circuit of a small-sized ANN (a two-layer perceptron with 32 memristive elements) has been constructed. An algorithm for formation of weighting coefficients (ANN training), which takes into account probable spread of technological parameters of memristive structures has been developed.

Formation of Weighting Coefficients in an Artificial Neural Network Based on the Memristive Effect in Metal–Oxide–Metal Nanostructures

We propose an algorithm for preprocessing of quadrature phase-shift-keyed signals. The aim of preprocessing is to extract the informative component when filtering harmonic filling of a signal. The algorithm is based on generalization of the minimum variance approach to the case of nonlinear filtering. Application of the proposed scheme in the problem of mutual time delay estimation leads to a significant decrease in the computation time compared with the conventional methods in the presence of the Doppler effect.

An algorithm for digital preprocessing of QPSK signals in the problem of mutual time delay estimation

We discuss a theoretical informational approach to solving ill-posed problems of function recovery based on the use of the maximum entropy principle. On this basis, we propose an efficient computational algorithm for implementing the procedure of solving the function-recovery problem and the method of regularization of the problem of function recovery from the convolution. In many cases, the proposed regularization method ensures high recovery quality. If the recovery accuracy is insufficient, then the Largange multipliers obtained by this method can be the best initial approximation (in the sense of the least squares) for their iterative refinement.

O. A. Morozov

Papers

Multilayer Metal-Oxide Memristive Device with Stabilized Resistive Switching

One-Board Design and Simulation of Double-Layer Perceptron Based on Metal-Oxide Memristive Nanostructures

Formation of Weighting Coefficients in an Artificial Neural Network Based on the Memristive Effect in Metal–Oxide–Metal Nanostructures

An algorithm for digital preprocessing of QPSK signals in the problem of mutual time delay estimation

An Efficient Computational Algorithm for Implementing the Maximum Entropy Method in Deconvolution Inversion Problems