A5091785597- Advanced Memory and Neural Computing
- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
- Neural dynamics and brain function
- Conducting polymers and applications
- Ferroelectric and Negative Capacitance Devices
- Nanoparticles: synthesis and applications
- Transition Metal Oxide Nanomaterials
- Silicon Nanostructures and Photoluminescence
- Neural Networks and Reservoir Computing
- Nanowire Synthesis and Applications
- Magnetic properties of thin films
- Heavy Metal Exposure and Toxicity
- Electrochemical Analysis and Applications
- Anesthesia and Neurotoxicity Research
- Theoretical and Computational Physics
- Carbon Nanotubes in Composites
- Semiconductor materials and devices
- stochastic dynamics and bifurcation
- Agricultural Productivity and Crop Improvement
- Human Health and Disease
- Agriculture and Biological Studies
- Analytical Chemistry and Sensors
- Crop Yield and Soil Fertility
- Metallic Glasses and Amorphous Alloys
Kurchatov Institute
2016-2025
Moscow Aviation Institute
1990-2023
Moscow Power Engineering Institute
2019-2023
Moscow Institute of Physics and Technology
2014-2023
Ural Federal University
2018
State Scientific Center of the Russian Federation - Federal Medical Biophysical Center named after A.I. Burnazyan
2017
Federal Medical-Biological Agency
2017
Lomonosov Moscow State University
2006-2015
Institute of Materials for Electronics and Magnetism
2015
University of Parma
2015
Here we provide a perspective concept of neurohybrid memristive chip based on the combination living neural networks cultivated in microfluidic/microelectrode system, metal-oxide devices or arrays integrated with mixed-signal CMOS layer to control analog circuits, process decoded information, and arrange feedback stimulation biological culture as parts bidirectional neurointerface. Our main focus is state-of-the-art approaches for cultivation spatial ordering network dissociated hippocampal...
In this paper, the resistive switching and neuromorphic behavior of memristive devices based on parylene, a polymer both low-cost safe for human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti about 500 nm thickness). These organic exhibit excellent performance: low voltage (down to 1 V), large OFF/ON resistance ratio (about 10^3),...
Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they resistors that keep memory their previous conductive state. Whereas demonstrations simple (e.g., a single-layer perceptron) based on memristors already exist, more complicated is challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR task) using network...
Resistive and capacitive switching in capacitor metal/nanocomposite/metal (M/NC/M) structures based on (CoFeB)x(LiNbO3)100-x NC fabricated by ion-beam sputtering with metal content x $\approx$ 8-20 at. % is studied. The peculiarity of the structure synthesis was use increased oxygen ($\approx$ 2*10^-5 Torr) at initial stage growth. films, along nanogranules 3-6 nm size, contained a large number dispersed Co (Fe) atoms (up to ~10^22 cm^-3). Measurements were performed both DC AC (frequency...
Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence with efficient information processing. However, a major bottleneck in physical implementation these is strong dependence their performance unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) memristive devices. Recently, reservoir computing (RC) and spiking (SNSs) separately proposed as valuable options partially mitigate this...
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance significantly more energy-efficient approach to the implementation different types neural network algorithms than traditional hardware with Von-Neumann architecture. However, weight adjustment in formal neuromorphic networks by standard back-propagation techniques suffers from poor device-to-device reproducibility. One most promising approaches overcome this problem is use...
Polyaniline (PANI) based memristive devices have emerged as promising candidates for hardware implementation of artificial synapses (the key components neuromorphic systems) due to their high flexibility, low cost, solution processability, three-dimensional stacking capability, and biocompatibility. Here, we report on a way the significant improvement switching rate endurance PANI-based devices. The reduction PANI active channel dimension leads increase in resistive by hundreds times...
We present results of an experimental study structural, magnetotransport, and magnetic properties a disordered system which consists the strained crystalline CoFe nanogranules with size 2--5 nm embedded into B-Al-O oxide matrix large number dispersed Fe or Co atoms. They act in as ions contribute essentially to magnetization at $T$ \ensuremath{\leqslant} 25 K. The conductivity follows $\text{ln}T$ law on metallic side metal-insulator transition wide range metal content variation...
The development of memristor-based spiking neuromorphic systems (NS) has been essentially driven by the hope to replicate extremely high energy efficiency biological systems. Spike-timing-dependent plasticity (STDP) mechanism is considered as one most promising learning rules for NS. STDP observed in different types synapses presence neuromodulators, e.g. dopamine, and believed be an enabling phenomenon important functions such associative reinforcement learning. However, direct window...
Currently, there is growing interest in wearable and biocompatible smart computing information processing systems that are safe for the human body. Memristive devices promising solving such problems due to a number of their attractive properties, as low power consumption, scalability, multilevel nature resistive switching (plasticity). The plasticity allows memristors emulate synapses hardware neuromorphic (NCSs). aim this work was study Cu/poly-p-xylylene(PPX)/Au memristive elements...
Reliable parylene–PbTe memristors controlled via electrical and optical stimuli replicate key synaptic functions are applicable in neuromorphic computing systems.
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 complex SNNs with recurrent connections, comparable in efficiency back-propagation techniques capable unsupervised training. Here we suppose that each neuron biological network tends maximize its activity competition other neurons, put this principle at the basis new SNN algorithm. In such way,...
Spiking neuromorphic networks (SNNs) are bio-inspired artificial systems capable of unsupervised learning and promising candidates to mimic biological neural in efficient solution cognitive tasks. Most SNNs based on local rules, such as bio-like spike-time-dependent plasticity (STDP). In this paper, we report a significantly improved timescale STDP for polyaniline-based memristive microdevices. We have used result show the possibility associative with an STDP-like mechanism simple SNN. The...
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, functional similarity to biological synapses. Most studies on memristor-based use either software models memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting fixed pre-trained explainable feature...