scholarly journals Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Nanomaterials ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2860
Author(s):  
Yu Wang ◽  
Xintong Chen ◽  
Daqi Shen ◽  
Miaocheng Zhang ◽  
Xi Chen ◽  
...  
Keyword(s):  
Two Dimensional ◽  
Atom Type ◽  
Computing Systems ◽  
Artificial Neuron ◽  
Integrate And Fire ◽  
Threshold Switching ◽  
Artificial Neurons ◽  
Leaky Integration ◽  
Electrical Components ◽  
Lif Neuron

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

A Scalable Artificial Neuron Based on Ultrathin Two-Dimensional Titanium Oxide

ACS Nano ◽  
2021 ◽  
Author(s):  
Jingyun Wang ◽  
Changjiu Teng ◽  
Zhiyuan Zhang ◽  
Wenjun Chen ◽  
Junyang Tan ◽  
...  
Keyword(s):  
Titanium Oxide ◽  
Two Dimensional ◽  
Artificial Neuron

First demonstration of “Leaky Integrate and Fire” artificial neuron behavior on (V0.95Cr0.05)2O3 thin film

2018 ◽  
Vol 8 (03) ◽  
pp. 835-841 ◽  
Author(s):  
Coline Adda ◽  
Laurent Cario ◽  
Julien Tranchant ◽  
Etienne Janod ◽  
Marie-Paule Besland ◽  
...  
Keyword(s):  
Thin Film ◽  
Artificial Neuron ◽  
Integrate And Fire ◽  
Image Position

Abstract

Various Threshold Switching Devices for Integrate and Fire Neuron Applications

2019 ◽  
Vol 5 (9) ◽  
pp. 1800866 ◽  
Author(s):  
Donguk Lee ◽  
Myonghoon Kwak ◽  
Kibong Moon ◽  
Wooseok Choi ◽  
Jaehyuk Park ◽  
...  
Keyword(s):  
Integrate And Fire ◽  
Threshold Switching ◽  
Integrate And Fire Neuron ◽  
Switching Devices

scholarly journals Interspike Interval Correlations, Memory, Adaptation, and Refractoriness in a Leaky Integrate-and-Fire Model with Threshold Fatigue

2003 ◽  
Vol 15 (2) ◽  
pp. 253-278 ◽  
Author(s):  
Maurice J. Chacron ◽  
Khashayar Pakdaman ◽  
André Longtin
Keyword(s):  
Chaotic Behavior ◽  
Initial Conditions ◽  
Discharge Rate ◽  
Correlation Coefficients ◽  
Stimulus Onset ◽  
Constant Current ◽  
Integrate And Fire ◽  
Sinusoidal Currents ◽  
Lif Neuron ◽  
Sensitivity To Initial Conditions

Neuronal adaptation as well as interdischarge interval correlations have been shown to be functionally important properties of physiological neurons. We explore the dynamics of a modified leaky integrate-and-fire (LIF) neuron, referred to as the LIF with threshold fatigue, and show that it reproduces these properties. In this model, the postdischarge threshold reset depends on the preceding sequence of discharge times. We show that in response to various classes of stimuli, namely, constant currents, step currents, white gaussian noise, and sinusoidal currents, the model exhibits new behavior compared with the standard LIF neuron. More precisely, (1) step currents lead to adaptation, that is, a progressive decrease of the discharge rate following the stimulus onset, while in the standard LIF, no such patterns are possible; (2) a saturation in the firing rate occurs in certain regimes, a behavior not seen in the LIF neuron; (3) interspike intervals of the noise-driven modified LIF under constant current are correlated in a way reminiscent of experimental observations, while those of the standard LIF are independent of one another; (4) the magnitude of the correlation coefficients decreases as a function of noise intensity; and (5) the dynamics of the sinusoidally forced modified LIF are described by iterates of an annulus map, an extension to the circle map dynamics displayed by the LIF model. Under certain conditions, this map can give rise to sensitivity to initial conditions and thus chaotic behavior.

Solitary Waves of Integrate-and-Fire Neural Fields

1997 ◽  
Vol 9 (8) ◽  
pp. 1677-1690 ◽  
Author(s):  
David Horn ◽  
Irit Opher
Keyword(s):  
Solitary Waves ◽  
Two Dimensional ◽  
Neural Fields ◽  
Firing Activity ◽  
Firing Patterns ◽  
Integrate And Fire ◽  
Strong Inhomogeneity ◽  
Continuum Formulation ◽  
Dimensional Surface

Arrays of interacting identical neurons can develop coherent firing patterns, such as moving stripes that have been suggested as possible explanations of hallucinatory phenomena. Other known formations include rotating spirals and expanding concentric rings. We obtain all of them using a novel two-variable description of integrate-and-fire neurons that allows for a continuum formulation of neural fields. One of these variables distinguishes between the two different states of refractoriness and depolarization and acquires topological meaning when it is turned into a field. Hence, it leads to a topologic characterization of the ensuing solitary waves, or excitons. They are limited to pointlike excitations on a line and linear excitations, including all the examples noted above, on a two dimensional surface. A moving patch of firing activity is not an allowed solitary wave on our neural surface. Only the presence of strong inhomogeneity that destroys the neural field continuity allows for the appearance of patchy incoherent firing patterns driven by excitatory interactions.

scholarly journals Expectation of Tourism Demand in Iraq by Using Artificial Neural Network

2019 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Rashid Anasari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tourism Demand ◽  
Artificial Neuron ◽  
Tourism Research ◽  
Prediction Bias ◽  
Alternative Approach ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
Travel Industry

This study survey and proves this effectiveness connected with artificial neural networks (ANNs) as an alternative approach in the tourism research. The learning utilizes the travel industry in the Japan being a method for estimating need to exhibit the solicitation. The outcome reveals the use of ANNs in tourism research might perhaps result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand examination is needed to establish and validate the effects. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

Artificial Neuron using Ag/2D-MoS2/Au Threshold Switching Memristor

2019 ◽  
Author(s):  
Durjoy Dev ◽  
Adithi Krishnaprasad ◽  
Zhezhi He ◽  
Sonali Das ◽  
Mashiyat Sumaiya Shawkat ◽  
...  
Keyword(s):  
Artificial Neuron ◽  
Threshold Switching

scholarly journals Artificial Neurons: Quasi‐Hodgkin–Huxley Neurons with Leaky Integrate‐and‐Fire Functions Physically Realized with Memristive Devices (Adv. Mater. 3/2019)

2019 ◽  
Vol 31 (3) ◽  
pp. 1970020
Author(s):  
He‐Ming Huang ◽  
Rui Yang ◽  
Zheng‐Hua Tan ◽  
Hui‐Kai He ◽  
Wen Zhou ◽  
...  
Keyword(s):  
Integrate And Fire ◽  
Artificial Neurons

High-speed Si films based threshold switching device and its artificial neuron application

2021 ◽  
Vol 119 (15) ◽  
pp. 153507
Author(s):  
Lei Yan ◽  
Yifei Pei ◽  
Jingjuan Wang ◽  
Hui He ◽  
Ying Zhao ◽  
...  
Keyword(s):  
High Speed ◽  
Artificial Neuron ◽  
Switching Device ◽  
Threshold Switching ◽  
Si Films

Critical Analysis of Two Dimensional and Four-Dimensional Spiking Neuron Models

2019 ◽  
Vol 16 (9) ◽  
pp. 3897-3905
Author(s):  
Pankaj Kumar Kandpal ◽  
Ashish Mehta
Keyword(s):  
Error Correction ◽  
Neuron Model ◽  
Learning Algorithm ◽  
Two Dimensional ◽  
Spiking Neuron ◽  
Classification Rate ◽  
Integrate And Fire ◽  
Study Results ◽  
Spiking Neuron Model ◽  
Integrate And Fire Neuron

In the present article, two-dimensional “Spiking Neuron Model” is being compared with the fourdimensional “Integrate-and-fire Neuron Model” (IFN) using error correction back propagation learning algorithm (error correction learning). A comparative study has been done on the basis of several parameters like iteration, execution time, miss-classification rate, number of iterations etc. The authors choose the five-bit parity problem and Iris classification problem for the present study. Results of simulation express that both the models are capable to perform classification task. But single spiking neuron model having two-dimensional phenomena is less complex than Integrate-fire-neuron, produces better results. On the contrary, the classification performance of single ingrate-and-fire neuron model is not very poor but due to complex four-dimensional architecture, miss-classification rate is higher than single spiking neuron model, it means Integrate-and-fire neuron model is less capable than spiking neuron model to solve classification problems.

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