Draft:Victor Erokhin

{{AFC comment|1=The article makes many claims about his notability. If the wider community accepted them he would have a massive h-factor and a string of major awards. However, he has neither. This indicates that the general community is not convinced, so as a reviewer, I am also not. I don't think he is close. Ldm1954 (talk) 12:16, 11 March 2024 (UTC)} Nevertheless, the reviewer Laurence D. Marks has not a massive h-factor, only 77, but he has a wiki page. Weird. External evidences gives support to Prof. Erokhin ideas and facts. It is not a competition, just a biographical description of the research of a scientist

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Victor Erokhin is an Italian-Russian physicist and material scientist, known for his works in organic memristive devices, and the inventor of the first in the world device that is described via universal memristive phenomena memristive device. He is a Research Director of the CNR-IMEM (CNR - Istituto dei Materiali per l'Elettronica ed il Magnetismo). Dr. Erokhin's work ranges from neuromorphic applications of organic-based memristive devices to the realization of polymeric smart drug delivery systems. His group demonstrated the capability of reproducing the nervous system of the snail Lymnaea stagnal, the realization of a hardware version of an artificial neural network (single and multilayer perceptron)., the realization of a functional connection between two cells of the rat brain slice and in 2023 Victor Erokin and Max Talanov and their teams demonstrated the proof of concept of the feedback-driven self-learning segment of the spinal CPG as the piece of hardware .

In the drug delivery application, his group reported the realization of microcapsules realized employing Layer by layer method, capable of loading and selectively realizing their content depending on the pH, and of targeting by magnetic force.

Specifically

In 2006 Victor Erokhin and Marco Fontana fabricated what could be later identified as organic polyaniline (PANI) memristive device two years before R. Stanley Williams from Hewlett Packard announced the discovery of a titanium dioxide memristive device.

Later in 2006 Victor Erokhin and Marco Fontana fabricated the schematic circuit of the adaptive networks composed of eight discrete nonlinear electrochemical elements similar to the nervous system of the snail Lymnaea stagnal comprising synthetic analogues of neurons and synapses. They demonstrated the system's capability for learning with a model of the simplest network composed of eight electrochemical elements. An alternative approach based on the formation of fiber networks was proposed. Authors proposed the approach to fabricate more complex systems with a random distribution of mutual contacts between elements .

In 2015 Victor Erokhin in collaboration with Vyacheslav Demin published results of the memristive implementation of the perceptron demonstrating effective training .

Later in 2018 Victor Erokhin and Roustem Khazipov demonstrated the memristive synaptic prosthesis connecting two cells of the rat brain slice, not connected naturally by biological synapse. They managed to demonstrate not only the connection but the self-learning capability of the PANI memristive devices similar to biological synapses, paving the way to memristive neuromorphic prosthetics

In 2020 Victor and several groups of scientists worldwide published a comprehensive review of modern approaches on neuro hybrid systems combining spiking memristive and traditional CMOS approaches with biological nervous system .

Later in 2023 Victor Erokin and Max Talanov and their respective teams demonstrated the proof of concept of the feedback-driven self-learning segment of the spinal Central Pattern Generator as the piece of hardware .

Other interestig articles

1. Electrochemical Control of the Conductivity in an Organic Memristor: A Time-Resolved X-ray Fluorescence Study of Ionic Drift as a Function of the Applied Voltage. (2009) https://doi.org/10.1021/am900464k
2. Optimization of an organic memristor as an adaptive memory element. (2009) https://doi.org/10.1063/1.3153944
3. Stochastic hybrid 3D matrix: learning and adaptation of electrical properties. (2012) https://doi.org/10.1039/C2JM35064E
4. First steps towards realising a double-layer perceptron based on organic memristive devices. (2016) https://doi.org/10.1063/1.4966257
5. Polyaniline-based memristive microdevice with high switching rate and endurance. (2018) https://doi.org/10.1063/1.5013929
6. Parylene-Based Memristive Devices with Multilevel Resistive Switching for Neuromorphic Applications. (2019) https://doi.org/10.1038/s41598-019-47263-9
7. Effects of noise sourcing on organic memristive devices (2020). https://doi.org/10.1016/j.chaos.2020.110319