A5000642198- Advanced Memory and Neural Computing
- Photoreceptor and optogenetics research
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Conducting polymers and applications
- Neural Networks and Reservoir Computing
- Electric Power Systems and Control
- Industrial Engineering and Technologies
- Laser Design and Applications
- Advanced Energy Technologies and Civil Engineering Innovations
- Nuclear Physics and Applications
- Particle accelerators and beam dynamics
- Electromagnetic Effects on Materials
- Laser Material Processing Techniques
- Graphite, nuclear technology, radiation studies
- Laser-Matter Interactions and Applications
- Nuclear reactor physics and engineering
- Chemical Synthesis and Characterization
- Chemical and Physical Properties of Materials
- Transition Metal Oxide Nanomaterials
- Advanced Power Generation Technologies
- Ocular and Laser Science Research
- Semiconductor materials and interfaces
- Electron and X-Ray Spectroscopy Techniques
- Radioactive element chemistry and processing
Kurchatov Institute
2019-2024
Institute of Physics and Technology
2023
Moscow Power Engineering Institute
2019-2023
Moscow Aviation Institute
2020-2023
Moscow Institute of Physics and Technology
2019-2023
Russian Academy of Sciences
2009-2018
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...
One of the remarkable features emerging neuromorphic systems is ability implementing in‐memory computing which demonstrated using memristors to realize both memory and computation functionalities within a single element. However, biological neural exhibit many other outstanding capabilities, among one sensitivity temporal parameters activity. The identification realization able imitate this still very challenging perspective. Herein, polyaniline‐based organic memristive devices endowed with...
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...
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 stimulation series. An obvious lack adaptation capability stimulators, as well poor biocompatibility high power consumption prosthetic devices, highlights the need for medical usage neuromorphic systems including memristive devices. The latter electrical devices providing a wide...
Reservoir computing systems are promising for application in bio-inspired neuromorphic networks as they allow the considerable reduction of training energy and time costs well an overall system complexity. Conductive three-dimensional structures with ability reversible resistive switching intensively developed to be applied such systems. Nonwoven conductive materials, due their stochasticity, flexibility possibility large-scale production, seem this task. In work, fabrication a 3D material...
Organic memristive devices are promising elements to be used in memory tasks, neuromorphic systems, and bioelectronics as hardware analogs of synapses. Among other materials, small molecules considered this field candidates with more adjustable properties fine chemical structures. Herein, the first organic device based on benzothieno[3,2‐b][1]‐benzothiophene (BTBT) siloxane dimer is reported. The resistance programming by voltage pulses various control parameters demonstrated: amplitude,...
This paper gives an analysis of the possibility attenuating pulsed light fluxes reflective surfaces by subjecting them to nanosize deformations excited powerful laser radiation. The proposed physical model is used as a basis for discussing thermophysical aspects appearance thermally induced deformations, characteristic times their existence, and functional characteristics when they interact with optical
Memristive devices offer essential properties to become a part of the next-generation computing systems based on neuromorphic principles. Organic memristive exhibit unique set which makes them an indispensable choice for specific applications, such as interfacing with biological systems. While switching rate organic can be easily adjusted over wide range through various methods, controlling potential is often more challenging, this parameter intricately tied materials used. Given limited...
Investigations of technical level low-gas energetic condensed systems ensuring activation reserve current sources have been carried out the areas their application were justified; formulations, manufacturing technology and peculiarities functioning reviewed.
The article considers the receiving and research of physical chemical properties polymeric electrolyte with Li-ion conductivity. There is rather detailed description technological process production, conductivity in a wide interval temperatures, also infrared spectroscopy X-ray diffraction analysis. It shown that can be applied at production energy converters film structure.
ABSTRACT. Memristive devices have a multitude of potential applications, ranging from neuromorphic computing systems and chips to bioprosthetic, each demanding distinct characteristics features. Among these attributes, the time resistive switching stands out as one most important items. Achieving synchronization between rates memristive existing that they complement, either CMOS or biological, is crucial importance. Moreover, defines energy consumption. Organic devices, particularly those...
In recent years, many scientific groups have been working on hardware implementation of the artificial neural networks to approach computational efficiency their biological counterpart. Memristors may play role synapses in such [1]. Varieties memristive structures and materials already tested different network architectures, but still no memristor is considered ideal for synapse One most significant problems presence inherent stochasticity distinctive all devices, which complicates training...