How to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation

Kang, Sa-Ouk; Choi, Donghyun; Eshraghian, Jason K.; Peng, Zhou; Kim, Jieun; Kong, Bai-Sun; Zhu, Xiaojian; Demirkol, Ahmet Şamil; Ascoli, Alon; Tetzlaff, Ronald; Lü, Wei; Chua, Leon O.

doi:10.1109/tcsi.2021.3126555

Cited by 34 publications

(15 citation statements)

References 52 publications

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“…The simulations results showed that although nerve cells have a very complex structure, AP has a stereotyped form in nerve cells and is formed according to the "all or nothing" principle. This principle is of critical importance and demonstrates that realistic neuronal models can also be applied to digital circuits An example of hardware designs made for this purpose is the I&F-based Memristive Integrate-and-Fire (MIF) model [31].…”

Section: Discussionmentioning

confidence: 99%

Generation and Transmission of Action Potential in Nerve Cells and Neuron Populations Based on the Realistic Hodgkin-Huxley Neuron Model

Tekin

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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There are several types of nerve cells in the central nervous system. Thanks to the synaptic connections, these cells form large and complicated networks. However, these cells have a stereotypical electrical activity called action potential (AP) or spike. In this work, the mechanisms of formation of this typical electrical signal and the methods of transferring from one cell to another were investigated using Hodgkin-Huxley neuron model simulations. It has been seen that the formation of AP is based on the principle of "all or nothing" and that ion channel dynamics are critical in the typical form of AP. It has been shown that signal transduction between nerve cells is transmitted by post-synaptic potential and that these signals may be cell depolarizing or polarizing. Finally, it is discussed that these electrical activities are quantities that can be measured at micro and macro levels, and various methods are used for this purpose.

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Section: Discussionmentioning

confidence: 99%

Generation and Transmission of Action Potential in Nerve Cells and Neuron Populations Based on the Realistic Hodgkin-Huxley Neuron Model

Tekin

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…For example, [59] introduces a combination of exponential functions and a sinusoid function to model the interfacial energy and periodicity of the transport dynamics respectively. While the model accuracy issue caused by numerical integration of model equations have been addressed [60,61] and there are ongoing efforts to develop accurate models for specific applications [62][63][64], the multi-state switching behaviour has not been modelled to the comprehensiveness required for robust circuit design. With more and more characterization data becoming available, it is expected that the memristor model will be extended to cover switching metastability, temperature dependencies, and also the electroforming process [65].…”

Section: B Memristor Modellingmentioning

confidence: 99%

Multi-State Memristors and Their Applications: An Overview

Wang

Jiang

et al. 2022

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Memristors show great potential for being integrated into CMOS technology and provide new approaches for designing computing-in-memory (CIM) systems, brain-inspired applications, trimming circuits and other topologies for the beyond-CMOS era. A crucial characteristic of the memristor is multi-state 1 switching. Memristors are capable of representing information in an ultra-compact fashion, by storing multiple bits per device. However, certain challenges remain in multistate memristive circuits and systems design such as device stability and peripheral circuit complexity. In this paper, we review the state of the art of multi-state memristor technologies and their associated CMOS/Memristor circuit design, and discuss the challenges regarding device imperfection factors, modelling, peripheral circuit design and layout. We present measurement results of our in-house fabricated multi-state memristor as an example to further illustrate the feasibility of applying multistate memristors in CMOS design, and demonstrate their related future applications such as multi-state memristive memories in machine learning, memristive neuromorphic applications, trimming and tuning circuits, etc. In the end, we summarize past and present efforts done in this field and envisage the direction of multi-state memristor related research.

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“…Significant research has shown the capacity of a memristor to be used as the synapses of a spiking neural network, even with the STDP models. [ 14–18 ] Scientists have successfully shown that various materials such as carbon, chalcogenides, metal oxides, amorphous silicon, and polymer–nanoparticle composite materials exhibit memristive properties. In addition, it has already been shown that memristor synapses can support important functions such as STDP when integrated with metal–oxide–semiconductor neurons.…”

Section: Memristor Devices As Synapses In a Spiking Neural Networkmentioning

confidence: 99%

“…Significant research has shown the capacity of a memristor to be used as the synapses of a spiking neural network, even with the STDP models. [14][15][16][17][18] Scientists have successfully shown that various materials such as carbon, chalcogenides, metal oxides, amorphous silicon, and polymernanoparticle composite materials exhibit memristive properties.…”

Section: Memristor Devices As Synapses In a Spiking Neural Networkmentioning

confidence: 99%

Nanoscale Memristor‐Based Spike Timing‐Dependent Plasticity Learning in a Radix‐X Quantized Retinal Neural Network

Kim

Eshraghian

Goo

et al. 2022

Physica Status Solidi (a)

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The human retina sends visual signals to the brain's visual cortex from photoreceptors (rod and cone cells) through various synaptic pathways and performs crucial early vision processing before signals are passed to higher brain regions. Herein, an artificial retina system implemented based on the leaky integrate‐and‐fire spiking neuron model is presented. The architecture of the proposed retina system consists of a multilayer convolutional neural network (CNN), and the system uses spike timing‐dependent plasticity (STDP) as a feedforward learning rule. In addition, the system integrates a feedback plasticity learning rule to expedite learning convergence. The system weights are implemented using nanoscale memristor arrays, taking on a constrained (radix‐X) range of conductance states. The proposed system produces an output image of 25 × 25 pixels, corresponding to the output retina ganglion cells that act as the interface between the retina and the visual cortex, using an input image of 100 × 100 pixels.

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How to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation

Cited by 34 publications

References 52 publications

Generation and Transmission of Action Potential in Nerve Cells and Neuron Populations Based on the Realistic Hodgkin-Huxley Neuron Model

Generation and Transmission of Action Potential in Nerve Cells and Neuron Populations Based on the Realistic Hodgkin-Huxley Neuron Model

Multi-State Memristors and Their Applications: An Overview

Nanoscale Memristor‐Based Spike Timing‐Dependent Plasticity Learning in a Radix‐X Quantized Retinal Neural Network

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