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      Is Open Access

      Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

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          Abstract

          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.

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          Most cited references145

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            The missing memristor found.

            Anyone who ever took an electronics laboratory class will be familiar with the fundamental passive circuit elements: the resistor, the capacitor and the inductor. However, in 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor). Although he showed that such an element has many interesting and valuable circuit properties, until now no one has presented either a useful physical model or an example of a memristor. Here we show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current-voltage behaviour observed in many nanoscale electronic devices that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches.
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              Memristor-The missing circuit element

              L P Chua (1971)
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                28 April 2020
                2020
                : 14
                : 358
                Affiliations
                [1] 1Lobachevsky State University of Nizhny Novgorod , Nizhny Novgorod, Russia
                [2] 2Department of Information Technologies, Vladimir State University , Murom, Russia
                [3] 3Neuroscience Laboratory, Kazan Federal University , Kazan, Russia
                [4] 4Department of Neurologic Surgery, Mayo Clinic , Rochester, MN, United States
                [5] 5Laboratory of Motor Neurorehabilitation, Kazan Federal University , Kazan, Russia
                [6] 6Kurchatov Institute , Moscow, Russia
                [7] 7CNR-Institute of Materials for Electronics and Magnetism, Italian National Research Council , Parma, Italy
                [8] 8Center for Technologies in Robotics and Mechatronics Components, Innopolis University , Innopolis, Russia
                [9] 9Cell Technology Group, Privolzhsky Research Medical University , Nizhny Novgorod, Russia
                [10] 10Institute of Microelectronics, Tsinghua University , Beijing, China
                [11] 11Dipartimento di Fisica e Chimica-Emilio Segrè, Group of Interdisciplinary Theoretical Physics, Università di Palermo and CNISM, Unità di Palermo , Palermo, Italy
                [12] 12Istituto Nazionale di Fisica Nucleare, Sezione di Catania , Catania, Italy
                Author notes

                Edited by: Stefano Brivio, Institute for Microelectronics and Microsystems (CNR), Italy

                Reviewed by: Jessamyn Fairfield, National University of Ireland Galway, Ireland; Robert Rieger, University of Kiel, Germany

                *Correspondence: Alexey Mikhaylov, mian@ 123456nifti.unn.ru

                This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2020.00358
                7199501
                32410943
                aedb956e-7fad-4d1e-b49d-16e6360e199b
                Copyright © 2020 Mikhaylov, Pimashkin, Pigareva, Gerasimova, Gryaznov, Shchanikov, Zuev, Talanov, Lavrov, Demin, Erokhin, Lobov, Mukhina, Kazantsev, Wu and Spagnolo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 October 2019
                : 24 March 2020
                Page count
                Figures: 2, Tables: 0, Equations: 0, References: 145, Pages: 14, Words: 0
                Funding
                Funded by: Russian Science Foundation 10.13039/501100006769
                Award ID: 16-19-00144
                Award ID: 18-29-23041
                Award ID: 18-44-160032
                Award ID: 19-29-03057
                Funded by: Российский Фонд Фундаментальных Исследований (РФФИ) 10.13039/501100002261
                Award ID: 18-29-23001
                Award ID: 18-29-23041
                Award ID: 18-44-160032
                Award ID: 19-29-03057
                Funded by: МЕГАГРАНТЫ 10.13039/501100012189
                Award ID: 074-02-2018-330 (2)
                Categories
                Neuroscience
                Perspective

                Neurosciences
                memristor,neuronal culture,spiking neural network,microfluidics,biosensor,neuroprosthetics

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