Computational neuroscience
Dynamic neural modeling for patient-specific recovery trajectories.
Scientific Foundation
Research-led neural digital twin development
NeuroTwin is grounded in computational neuroscience, neuromorphic computing, synaptic plasticity research, and clinical rehabilitation collaboration.
Research areas
Post-stroke rehabilitation modeling
Motor recovery prediction, trajectory assessment, and risk identification.
Neuromorphic computing
Event-driven, memory-compute-oriented architectures for scalable neural simulation.
Organic memristive devices
Synapse-like adaptive devices for bio-inspired learning and memory-like behavior.
Neurointerfaces and future prosthetic directions
Long-term research into adaptive interfaces between biological and artificial neural systems.
Selected references from our team
Critical Analysis of Energy Consumption in Neuro-Computational Systems
Ivan Kipelkin, Ilija Kamenko, Jovan Ivošević, Alina Fedorova, Grigory Zharkov, Jovana Maricic, Stojanka Bratic, Nebojša Pilipović, Natasa Samardzic, Staniša Dautović, Milovan Medojević, Branimir Bajac, Jordi Vallverdú, Dragiša Žunić, Francesco Restuccia, Vincenzo Alessio, Francesco Longo, Giovanni Merlino, Dario Bruneo, Salvatore Distefano, Alexander Toschev, Alexey Mikhaylov, Victor Erokhin, Max Talanov
IEEE Access, 2026
A unified benchmark of energy consumption per synaptic event across GPUs, NPUs, FPGAs, digital spiking processors, memristive devices, and biological reference ranges.
Read the articlePrinting Polyaniline Based Organic Memristive Devices for Neuromorphic Computing Applications
Silvia Battistoni, Anna N. Matsukatova, Rocco Carcione, Luciano Ferrucci, Matteo Parmeggiani, Matteo Cocuzza, Simone Luigi Marasso, Andrey V. Emelyanov, Vyacheslav A. Demin, Victor Erokhin
Materials Today Chemistry, 2026
A study of printable polyaniline-based organic memristive devices and their potential as scalable, adaptable hardware elements for neuromorphic computing applications.
Read the articleAdvancing Neural Networks: Innovations and Impacts on Energy Consumption
Alina Fedorova, Nikola Jovišić, Jordi Vallverdù, Silvia Battistoni, Miloš Jovičić, Milovan Medojević, Alexander Toschev, Evgeniia Alshanskaia, Max Talanov, Victor Erokhin
Advanced Electronic Materials, 2024
A review comparing the energy consumption of conventional artificial neural networks, spiking neural networks, specialized memristive hardware, and the human brain.
Read the articleCombination of Organic-Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification
Anna N. Matsukatova, Nikita V. Prudnikov, Vsevolod A. Kulagin, Silvia Battistoni, Anton A. Minnekhanov, Andrey D. Trofimov, Aleksandr A. Nesmelov, Sergey A. Zavyalov, Yulia N. Malakhova, Matteo Parmeggiani, Alberto Ballesio, Simone Luigi Marasso, Sergey N. Chvalun, Vyacheslav A. Demin, Andrey V. Emelyanov, Victor Erokhin
Advanced Intelligent Systems, 2023
A fully organic system combining volatile polyaniline reservoir computing with a nonvolatile parylene-memristor spiking readout layer for robust spatiotemporal pattern classification.
Read the articleMemristive Circuit-Based Model of Central Pattern Generator to Reproduce Spinal Neuronal Activity in Walking Pattern
Dinar N. Masaev, Alina A. Suleimanova, Nikita V. Prudnikov, Mariia V. Serenko, Andrey V. Emelyanov, Vyacheslav A. Demin, Igor A. Lavrov, Max O. Talanov, Victor V. Erokhin
Frontiers in Neuroscience, 2023
A self-learning memristive circuit model that uses biologically plausible spike-timing-dependent plasticity to reproduce spinal neuronal activity associated with walking patterns.
Read the articleMemristive Devices for Neuromorphic Applications: Comparative Analysis
Victor Erokhin
BioNanoScience, 2020
A comparative review of organic and inorganic memristive devices for neuromorphic applications, including memory-processing integration, sensors, oscillators, bio-mimicking circuits, and living-system coupling.
Read the articleAssociative STDP-Like Learning of Neuromorphic Circuits Based on Polyaniline Memristive Microdevices
Nikita V. Prudnikov, Dmitry A. Lapkin, Andrey V. Emelyanov, Anton A. Minnekhanov, Yulia N. Malakhova, Sergey N. Chvalun, Vyacheslav A. Demin, Victor V. Erokhin
Journal of Physics D: Applied Physics, 2020
An experimental demonstration of improved STDP timescales and unsupervised associative learning in a simple spiking neural network built with polyaniline memristive microdevices.
Read the articleParylene Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications
Anton A. Minnekhanov, Andrey V. Emelyanov, Dmitry A. Lapkin, Kristina E. Nikiruy, Boris S. Shvetsov, Alexander A. Nesmelov, Vladimir V. Rylkov, Vyacheslav A. Demin, Victor V. Erokhin
Scientific Reports, 2019
A study of low-cost, biocompatible parylene memristors demonstrating low switching voltage, stable multilevel resistance states, and biologically inspired STDP training.
Read the articleCoupling Cortical Neurons Through Electronic Memristive Synapse
Elvira Juzekaeva, Azat Nasretdinov, Silvia Battistoni, Tatiana Berzina, Salvatore Iannotta, Rustem Khazipov, Victor Erokhin, Marat Mukhtarov
Advanced Materials Technologies, 2018
The first demonstration of activity-dependent coupling between two living cortical neurons through an organic memristive device acting as an artificial synapse.
Read the articleMaterial Memristive Device Circuits with Synaptic Plasticity: Learning and Memory
Victor Erokhin, Tatiana Berzina, Paolo Camorani, Anteo Smerieri, Dimitris Vavoulis, Jianfeng Feng, Marco P. Fontana
BioNanoScience, 2011
An experimental demonstration of organic memristive device circuits showing adaptive behavior inspired by synaptic plasticity and learning in a biological neural reference system.
Read the articleEuropean brain research ecosystem
Our research direction is aligned with the broader European brain research ecosystem, including EBRAINS, which provides data, tools, models, and services for brain-related research.
Interested in validating patient-specific neural digital twins in rehabilitation?
We are inviting neurorehabilitation clinics and research partners to explore retrospective and prospective collaboration opportunities.