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      Neuromorphic artificial intelligence systems


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          Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of currently available neuromorphic AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida, Mythic). Also, we present the principle of classifying neuromorphic AI systems by the brain features they use: connectionism, parallelism, asynchrony, impulse nature of information transfer, on-device-learning, local learning, sparsity, analog, and in-memory computing. In addition to reviewing new architectural approaches used by neuromorphic devices based on existing silicon microelectronics technologies, we also discuss the prospects for using a new memristor element base. Examples of recent advances in the use of memristors in neuromorphic applications are also given.

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          Learning representations by back-propagating errors

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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)

                Author and article information

                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                14 September 2022
                : 16
                : 959626
                [1] 1Cifrum , Moscow, Russia
                [2] 2Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University , Moscow, Russia
                [3] 3Faculty of Physics, Lomonosov Moscow State University , Moscow, Russia
                [4] 4Laboratory of Neuromorphic Computations, Department of Physics, Chuvash State University , Cheboksary, Russia
                Author notes

                Edited by: Lining Zhang, Peking University, China

                Reviewed by: Jiyong Woo, Kyungpook National University, South Korea; Joao Ventura, University of Porto, Portugal; Jamal Lottier Molin, Naval Sea Systems Command (NAVSEA), United States; Catarina Dias, University of Porto, Portugal

                *Correspondence: Dmitry Ivanov rudimiv@ 123456gmail.com

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

                Copyright © 2022 Ivanov, Chezhegov, Kiselev, Grunin and Larionov.

                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.

                : 01 June 2022
                : 17 August 2022
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 83, Pages: 20, Words: 13598

                neuromorphic computing,brain-inspired computing,neuromorphic,neuromorphic accelerator,memristor,neural network,ai hardware


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