Parylene-MoO crossbar memristors as a volatile reservoir and non-volatile readout: a homogeneous reservoir computing system.

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system.

Reliable Memristive Synapses Based on Parylene-MoO Nanocomposites for Neuromorphic Applications.

Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge.

Memristor-based spiking neural network with online reinforcement learning.

Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure.

Dynamics of the Proton Exchange Process in Benzoic Acid Interacting with Lithium Niobate Crystals.

This paper provides a numerical analysis of the behavior of the ion boundary layer formed during proton exchange. Thermal dissociation of benzoic acid melt molecules leads to the formation of benzoate ions and hydrogen ions. The latter can be absorbed by a lithium niobate wafer, with subsequent diffusion of lithium ions in the acid.

Multi-Terminal Nonwoven Stochastic Memristive Devices Based on Polyamide-6 and Polyaniline for Neuromorphic Computing.

Reservoir computing systems are promising for application in bio-inspired neuromorphic networks as they allow the considerable reduction of training energy and time costs as well as an overall system complexity. Conductive three-dimensional structures with the ability of reversible resistive switching are intensively developed to be applied in such systems. Nonwoven conductive materials, due to their stochasticity, flexibility and possibility of large-scale production, seem promising for this task.

Memristive circuit-based model of central pattern generator to reproduce spinal neuronal activity in walking pattern.

Existing methods of neurorehabilitation include invasive or non-invasive stimulators that are usually simple digital generators with manually set parameters like pulse width, period, burst duration, and frequency of stimulation series. An obvious lack of adaptation capability of stimulators, as well as poor biocompatibility and high power consumption of prosthetic devices, highlights the need for medical usage of neuromorphic systems including memristive devices. The latter are electrical devices providing a wide range of complex synaptic functionality within a single element.

Diamane-like Films Based on Twisted G/BN Bilayers: DFT Modelling of Atomic Structures and Electronic Properties.

Diamanes are unique 2D carbon materials that can be obtained by the adsorption of light atoms or molecular groups onto the surfaces of bilayer graphene. Modification of the parent bilayers, such as through twisting of the layers and the substitution of one of the layers with BN, leads to drastic changes in the structure and properties of diamane-like materials. Here, we present the results of the DFT modelling of new stable diamane-like films based on twisted Moiré G/BN bilayers.

Physical model and numerical simulation of high-temperature silicification of carbon composite material.

A mathematical model is developed to describe the process of high-temperature silicification of a carbon porous material. The cause of pores blockage is the condensation of gaseous silicon at the inner walls of tubules. Phenomenological temperature dependences for the coefficients of condensation and evaporation are proposed, which determine the intensity of the siliconizing process.

Ultra-Low Thermal Conductivity of Moiré Diamanes.

Ultra-thin diamond membranes, diamanes, are one of the most intriguing quasi-2D films, combining unique mechanical, electronic and optical properties. At present, diamanes have been obtained from bi- or few-layer graphene in AA- and AB-stacking by full hydrogenation or fluorination. Here, we study the thermal conductivity of diamanes obtained from bi-layer graphene with twist angle θ between layers forming a Moiré pattern.

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)(LiNbO) Nanocomposite Memristors.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization.

Exceptional warming over the Barents area.

In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20-40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea.

Moiré Diamones: New Diamond-like Films of Semifunctionalized Twisted Graphene Layers.

We proposed novel carbon nanostructures based on a twisted few-layered graphene with one side passivated by hydrogen or fluorine: Moiré diamones on graphene. The presence of a dangling bond at the bottom layer of diamones leads to the appearance of spin density localization, which can be tuned by the variation of the twist angle with the following formation of Moiré diamones. The spin-polarized nature of electronic density distribution was obtained and discussed in detail on the basis of ab initio calculations.

Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing.

Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs).

Semiconductor nanochannels in metallic carbon nanotubes by thermomechanical chirality alteration.

Carbon nanotubes have a helical structure wherein the chirality determines whether they are metallic or semiconducting. Using in situ transmission electron microscopy, we applied heating and mechanical strain to alter the local chirality and thereby control the electronic properties of individual single-wall carbon nanotubes. A transition trend toward a larger chiral angle region was observed and explained in terms of orientation-dependent dislocation formation energy.

Novel two-dimensional boron oxynitride predicted using the USPEX evolutionary algorithm.

Oxidation is a unique process that significantly changes the structure and properties of a material. Doping of h-BN by oxygen is a hot topic in material science leading to the possibility of synthesis of novel 2D structures with customized electronic properties. It is still unclear how the atomic structure changes in the presence of external atoms during the oxidation of h-BN.

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network.

This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (STDP) is demonstrated with a pair of hardware analog neurons connected through a (CoFeB)(LiNbO) nanocomposite memristor. Next, the learning convergence to a solution of binary clusterization task is analyzed in a wide range of memristive STDP parameters for a single-layer fully connected feedforward SNN.

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.

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.

Fluorinated graphene nanoparticles with 1-3 nm electrically active graphene quantum dots.

A new approach to creating a new and locally nanostructured graphene-based material is reported. We studied the electric and structural properties of partially fluorinated graphene (FG) films obtained from an FG-suspension and nanostructured by high-energy Xe ions. Local shock heating in ion tracks is suggested to be the main force driving the changes.

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above.

Parylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications.

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 10), retention (≥10 s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes).

Yeasts producing zeatin.

The present paper describes the first screening study of the ability of natural yeast strains to synthesize in culture the plant-related cytokine hormone zeatin, which was carried out using HPLC-MS/MS. A collection of 76 wild strains of 36 yeast species (23 genera) isolated from a variety of natural substrates was tested for the production of zeatin using HPLC-MS/MS. Zeatin was detected in more than a half (55%) of studied strains and was more frequently observed among basidiomycetous than ascomycetous species.

Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity.

Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions. However, there is a lack of learning algorithms for complex SNNs with recurrent connections, comparable in efficiency with back-propagation techniques and capable of unsupervised training. Here we suppose that each neuron in a biological neural network tends to maximize its activity in competition with other neurons, and put this principle at the basis of a new SNN learning algorithm.

Planar and 3D fibrous polyaniline-based materials for memristive elements.

The memristive elements constructed using polymers - polyaniline (PANI) and polyethyleneoxide (PEO) - could be assembled on planar thin films or on 3D fibrous materials. Planar conductive PANI-based materials were made using the Langmuir-Schaefer (LS) method, and the 3D materials - using the electrospinning method which is a scalable technique. We have analyzed the influence of PANI molar mass, natures of solvent and subphase on the crystalline structure, thickness and conductivity of planar LS films, and the influence of PANI molar mass and the PANI-PEO ratio on the morphological and structural characteristics of 3D fibrous materials.