Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Mishchenko, Mikhail; Bolshakov, Denis I.; Lukoyanov, Vitaly; Королев, Д. С.; Belov, A. I.; Гусейнов, Д. В.; Matrosov, V. V.; Kazantsev, Victor B.; Mikhaylov, A. N.

doi:10.1088/1361-6463/ac79de

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…Memristive nanodevices, such as resistive RAMs, memristors [20], or adaptive transistors [21], can provide the compact synapses required for more advanced neuromorphic circuits. Recent studies have demonstrated that these devices can replicate biological synapses' learning rules, such as spike timing-dependent plasticity (STDP), and even more complex plasticity rules crucial for brain-inspired learning [26][27][28][29][30]. By combining memristive synapses with CMOS neurons (or potentially memristor-based neurons in the future [31]), significant advancements in computing and highly efficient cognitive tasks could be achieved.…”

Section: Introductionmentioning

confidence: 99%

Model of Neuromorphic Odorant-Recognition Network

2023

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We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of “decoder” neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the “decoder” layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of “decoder” neurons does not exhibit specificity in recognizing odorants. The recognition of the ’odorant’ occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.

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Section: Introductionmentioning

confidence: 99%

Model of Neuromorphic Odorant-Recognition Network

2023

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“…Such architectures can rely on the rich dynamics of memristive devices and the self-organization of plastic memristive connections. [9] Their similarity to the living brain architectures will make it possible to take the next step towards the hybrid intelligence based on the symbiosis of electronic and biological subsystems. [10,11] In addition, there are many other applications, including stochastic computing and probabilistic networks, as well as hardware information security primitives, [12][13][14] in which memristive devices act as the sources of randomness and entropy.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Maldonado

Belov

Koryazhkina

et al. 2022

Physica Status Solidi (a)

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Variability is an inherent property of memristive devices based on the switching of resistance in a simple metal–oxide–metal structure compatible with the standard complementary metal–oxide–semiconductor fabrication process. For each specific structure, the variability should be measured and assessed as both the negative and positive factors for different applications of memristive devices. In this report, it is shown how this variability can be extracted and analyzed for such main parameters of resistive switching as the set and reset voltages/currents and how it depends on the methodology used and experimental conditions. The obtained results should be taken into account in the design and predictive simulation of memristive devices and circuits.

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“…In addition, two neuron-type generators connected by a memristive element were shown to be capable of synchronization. This synchronization between two coupled neuron-like generators is transient and highly reliant on the present condition of the memristive element [5].…”

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confidence: 99%

“…Кроме того, продемонстрирована синхронизация двух нейроноподобных генераторов, связанных через мемристический элемент. Эта синхронизация носит временный характер и сильно зависит от текущего состояния мемристивного элемента [5].…”

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Dynamics of two neuron-like generators with memristive connection

Bolshakov,

Mishchenko,

Belov

et al. 2023

Genes & Cells

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At present one of the most urgent tasks of interdisciplinary science is the design and research of neuromorphic devices. Such devices are most often used to create systems for processing various kinds of information with algorithms similar to the data processing algorithms of the human brain or the brain of animals. The development of such neuromorphic electronics will allow computing devices and information processing systems to be built based on new principles and with a high level of parallelism [1]. Neuromorphic devices require the development of electronic components: neurons and synapses. The paper [2] proposed a phase-locked loop system with a bandpass filter in the control circuit. A more detailed study of the mathematical model of such a system has shown missing equilibrium states corresponding to the synchronization mode of the phase-locked loop system, but there are self-oscillating modes of varying complexity. Self-oscillations observed in such system are similar to spike and burst oscillation of the neuron’s membrane potential. Hardware implementation [3] of the considered neuron like generator in the form of an electronic device demonstrated the possibility of reproducing the same dynamic modes as in the mathematical model [2]. A fundamental disadvantage of the proposed model [2] and its experimental implementation [3] – the absence of an excitable mode (by excitable we mean a dynamic system with a stable equilibrium state and a periodic pseudoorbit of large amplitude, passing in the vicinity of the equilibrium state), when pulse generation would only respond to external disturbance. At the same time, the vast majority of brain neurons are in the excitable subthreshold mode, and their generation is primarily caused by presence of multiple connection. One of the tasks of this work was to modification the existing model of the neuron-like generator in order to preserve the known dynamics and add a mode of excited oscillator. While solving this problem, a modification of the neuron like generator based on the phase-locked loop system with a band-pass filter in the control circuit was proposed and implemented as an electronic circuit. The modification eliminates the basic drawback of the initial model – inability to work in the excitable mode. The new dynamic mode with the absence of self-oscillations was obtained by adding an electronically controlled switch between the low- and high-pass filters in the control loop. Existence of the excitable mode and the existence of previously known self-oscillating modes of varying complexity was demonstrated experimentally: spike, burst, and chaotic modes was confirmed [4]. Another task in this work was to explore the dynamics of two neuron-like generators with memristive coupling. A second-order memristor model based on Chua's memristor was used as a model of synaptic connection. When solving this problem, nonlinear frequency dependences of the conductivity of the memristive element were found. This dependence has the same character for self-oscillating modes of varying complexity: spike and burst. In addition, it was demonstrated synchronization of two neuron-like generators connected through a memristive element. Synchronization of two coupled neuron-like generators is interim in nature and strongly depends on the current state of the memristive element [5].

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Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Cited by 13 publications

References 38 publications

Model of Neuromorphic Odorant-Recognition Network

Model of Neuromorphic Odorant-Recognition Network

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Dynamics of two neuron-like generators with memristive connection

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Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Cited by 13 publications

References 38 publications

Model of Neuromorphic Odorant-Recognition Network

Model of Neuromorphic Odorant-Recognition Network

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti Memristive Devices

Dynamics of two neuron-like generators with memristive connection

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A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices