Mathematical model of a neuromorphic network based on memristive elements

Morozov, A. Yu.; Abgaryan, K. K.; Ревизников, Д. Л.

doi:10.1016/j.chaos.2020.110548

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…These hardware platforms facilitate the advancement of computing systems that are inspired by the structure and functioning of the human brain. 252 The memristive synapses play a crucial role in facilitating brain-inspired computing by enabling the replication of synaptic plasticity and learning mechanisms within artificial neural networks. The promising attributes of energy efficiency, adaptability, and diversity make them a compelling technology for the development of neuromorphic hardware that can effectively execute intricate cognitive tasks while minimising power usage.…”

Section: Electrochemical-memristor-based Artificial Neurons and Synapsesmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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Section: Electrochemical-memristor-based Artificial Neurons and Synapsesmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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“…A detailed description of the model is given in refs. [19][20][21]35]. In this article, the model has been modified to take into account the interval nature of the variables.…”

Section: Simulation Modeling Of the Neuromorphic Networkmentioning

confidence: 99%

Interval Model of a Memristor Crossbar Network

Morozov

Abgaryan

Ревизников

2022

Physica Status Solidi (b)

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This article covers the modeling of memristive oxide‐based elements within the context of a complex simulation model of an analog self‐learning spiking neural network. According to the experimental data, the nature of the memristor operation is stochastic, as evidenced by the variation in the current–voltage characteristics for different resistive switching cycles. Often, the existing mathematical models of memristors do not fully reproduce the experimental results, which can adversely affect the accuracy of neuromorphic network simulation. It is proposed to use the interval approach to take into account the variations in the element characteristics. The idea is to introduce interval parameters into the mathematical model. Thus, the simulation results at each moment are interval estimates of phase variables. The values of interval parameters are calculated in such a way that the intervals completely contain the experimental data. When simulating the operation of the entire analog self‐learning spiking neural network, at each training epoch the parameters of the memristors are set randomly from the found intervals. The approbation of the proposed approach is carried out on the problem of pattern recognition by the crossbar architecture network with the LiNbO3‐based memristive elements.

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“…For storing synaptic weights, transistor-based synapses are primarily investigated with flash memory [26,27]. Hardware-based neuromorphic systems aim to implement deep neural networks (DNN) by emulating biological neurons and synapses [6]. Neurons are typically represented using CMOS circuits, while research on synapses is currently focused on various nonvolatile memory technologies.…”

Section: Introductionmentioning

confidence: 99%

Polysilicon-Channel Synaptic Transistors for Implementation of Short- and Long-Term Memory Characteristics

Baek

Kim

2023

Biomimetics

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The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler–Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms.

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Mathematical model of a neuromorphic network based on memristive elements

Cited by 15 publications

References 24 publications

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Interval Model of a Memristor Crossbar Network

Polysilicon-Channel Synaptic Transistors for Implementation of Short- and Long-Term Memory Characteristics

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