Search query: Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks

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abstract: Abstract Artificial neuron is an important part of constructing neuromorphic network in which information can be computed with high parallelism and efficiency like in the human brain. However, owing to the poor biological plausibility, artificial neurons based on traditional complementary metal oxide semiconductor (CMOS) platform fail to reveal the rich ion dynamics of the biological counterparts. Organic inorganic halide perovskites (OHPs) are prospective for imitating the ion dynamics on the membranes of biological neurons because of its intrinsic ion migration. Herein, a diffusive CH3NH3PbI3(MAPbI3) based memristor with superior amplitude frequency characteristics and highly linear conductivity modulation for more than 1000 states have been fabricated for the construction of a leaky integrate and fire (LIF) bio inspired neuron. The as designed LIF model can successfully emulate the leakage, spatiotemporal integration and firing functions in a biological neuron. Moreover, by connecting LIF neurons with a 2*2 non volatile Al2O3 based synaptic array, a simple spiking neural network (SNN) which is called the 3rd generation of neural network has been implemented at the hardware level to study the cognitive performance of the network. The SNN exhibits outstanding selective sensitivity to particular input sequence, indicating the excellent adaptability and versatility of the network for future applications of neuromorphic computing by utilizing novel ionotropic device.
author_names: Jia-Qin Yang

Ruopeng Wang

Zhanpeng Wang

Qin-Yuan Ma

Jing-Yu Mao

Yi Ren

Xiaoyang Yang

Ye Zhou

Su-Ting Han
title: Leaky integrate and fire neurons based on perovskite memristor for spiking neural networks
year: 2020

abstract: Designing and implementing artificial neuromorphic systems, which can provide biocompatible interfacing, or the human brain akin ability to efficiently process information, is paramount to the understanding of the human brain complex functionality. Energy efficient, low area, and biocompatible artificial neurons are key ubiquitous components of any large scale neural systems. Previous CMOS based neurons implementations suffer from scalability drawbacks and cannot naturally mimic the analog behavior. Memristor and phase changed neurons have variability induced instability drawbacks, and usually rely on additional CMOS circuitry. However, graphene, despite its ballistic transport, inherently analog nature, and biocompatibility, which provide natural support for biologically plausible neuron implementations has only been considered for Boolean logic implementations. In this paper, we propose an ultra compact, all graphene based nonlinear Leaky Integrate and Fire spiking neuron. By means of SPICE simulations, we validate its basic functionality and investigate the output spikes response under stochastic noisy input spike trains with a variable firing rate, from 20 to 200 spikes per second. Simulation results indicate neuron robustness to noisy scenarios, and neuronal output firing regularity. The small area and the low energy consumption, due to 200 mV supply voltage operation, can benefit the implementation of large scale neural networks, and the biologically plausible operating conditions (e.g. 2 ms and 100 mV spike duration and amplitude) can promote the interfacebility of graphene based artificial neurons with biological counterparts.
author_names: H J Wang

Nicoleta Cucu Laurenciu

Y Jiang

Sorin Dan Cotofana
title: Ultra Compact, Entirely Graphene Based Nonlinear Leaky Integrate and Fire Spiking Neuron
venue: 2020 IEEE International Symposium on Circuits and Systems (ISCAS)
year: 2020

abstract: Abstract This paper presents the design and implementation of a low power and ultrafast spike timing dependent plasticity (STDP) of the spiking neural network (SNN) in a crossbar structure based on the ferroelectric tunnel memristor (FTM) where the nonlinear switching behaviors are mathematically described by a physics based compact model. The FTM is used as 1R structure without a cell selector to implement the memristive synaptic weight multiplications. The implementation details for the fully asynchronous communication/learning of the FTM as an artificial synapse are demonstrated. The FTM dynamics and the designed leaky integrate and fire neurons are interconnected allowing a scalable STDP learning. FTM changes its resistance in crossbar structure depending on the spike timing. The achieved results of the proposed analog implementation prove that the application of spikes as neural actions manipulates the STDP, which follows a multiplicative type of learning rule. A comparison of STDP results based on FTM device used in 1R and 1T1R (i.e. with a select transistor) structures in a crossbar network is demonstrated. This system presents a good scaling behavior, a low temporal processing delay along with an ultrafast plasticity at a low energy processing cost ~0.125 pJ) This gives new opportunities for building compact, ultrafast and power efficient neuromorphic computing systems.
author_names: Fakhreddine Zayer

Wael Dghais

Mohamed Benabdeladhim

Belgacem Hamdi
title: Low power, ultrafast synaptic plasticity in 1R ferroelectric tunnel memristive structure for spiking neural networks
venue: AEU International Journal of Electronics and Communications
year: 2019

abstract: The brained inspired spiking neural networks (SNNs) which can be applied on visual information processing and speech recognition, is attracting great attention especially when combined with emerging electronic synapse (i.e. memristor) The neuron, transmitting and receiving spiking signals, is a vital component to realize the biological rules of SNNs, such as leaky integrate and fire (LIF) inhibitory period, winner takes all (WTA) and bidirectional transmission. In this work, we propose and implement a novel spiking neuron circuit based on complementarymetal oxide semiconductor (CMOS) technology. Experimental results show that the neuron circuit can generate spiking pulses, realize lateral inhibition and change the weight of synapse.
author_names: Hai-jun Liu

Jiwei Li

Qing-jiang Li

Jietao Diao
title: A CMOS based Neuron Circuit for Spiking Neural Networks with Memristive Synapse
venue: DEStech Transactions on Computer Science and Engineering
year: 2019

abstract: A memrsitor is a two terminal electronic device whose conductance can be precisely modulated by charge or flux through it. In this paper, we present a class of memristor based neural circuits comprising leaky integrate and fire (I F) neurons and memristor based learning synapses. Employing these neuron circuits and corresponding SPICE models, the properties of a two neurons network are shown to be similar to biology. During correlated spiking of the pre and post synaptic neurons, the strength of the synaptic connection increases. Conversely, it is diminished when the spiking is uncorrelated. This synaptic plasticity and associative learning is essential for performing useful computation and adaptation in large scale artificial neural networks. Finally, future circuit design and consideration are discussed with the memristor based neural networks.
author_names: Shiping Wen

Zhigang Zeng

Tingwen Huang
title: Associative Learning of Integrate and Fire Neurons with Memristor Based Synapses
venue: Neural Processing Letters
year: 2012

abstract: Abstract A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock Cooper Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor based synapse and Leaky Integrate and Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.
author_names: Yongchuang Huang

Junxiu Liu

Jim Harkin

Liam McDaid

Yuling Luo
title: An memristor based synapse implementation using BCM learning rule
venue: Neurocomputing
year: 2021

abstract: During the last half century, the tremendous development of computers based on von Neumann architecture has led to the revolution of the information technology. However, von Neumann computers are outperformed by the mammal brain in numerous data processing applications such as pattern recognition and data mining. Neuromorphic engineering aims to mimic brain like behavior through the implementation of artificial neural networks based on the combination of a large number of artificial neurons massively interconnected by an even larger number of artificial synapses. In order to effectively implement artificial neural networks directly in hardware, it is mandatory to develop artificial neurons and synapses. A promising advance has been made in recent years with the introduction of the components called memristors that might implement synaptic functions. In contrast, the advances in artificial neurons have consisted in the implementation of silicon based circuits. However, so far, a single component artificial neuron that will bring an improvement comparable to what memristors have brought to synapses is still missing. Here, a simple two terminal device is introduced, which can implement the basic functions leaky integrate and fire of spiking neurons. Remarkably, it has been found that it is realized by the behavior of strongly correlated narrow gap Mott insulators subject to electric pulsing.
author_names: P Stoliar

Julien Tranchant

Benoit Corraze

Etienne Janod

M P Besland

Federico Tesler

Marcelo J Rozenberg

Laurent Cario
title: A Leaky Integrate and Fire Neuron Analog Realized with a Mott Insulator
year: 2017

abstract: Abstract Neuromorphic systems consisting of artificial neurons and synapses can process complex information with high efficiency to overcome the bottleneck of von Neumann architecture. Artificial neurons are essentially required to possess functions such as leaky integrate and fire and output spike. However, previous reported artificial neurons typically have high operation voltage and large leakage current, leading to significant power consumption, which is contrary to the energy efficient biological model. Here, an oscillatory neuron based on Ag filamentary threshold switching memristor (TS) that has a low operation voltage <0.6 V) with ultralow power consumption <1.8 uW) is presented. It can trigger neuronal functions, including leaky integrate and fire and threshold driven spiking output, with high endurance >108 cycles) Being connected to an external resistor or a resistive switching memristor (RS) as synaptic weight, the TS clearly demonstrates self oscillation behavior once the input pulse voltage exceeds the threshold voltage. Meanwhile, the oscillation frequency is proportional to the input pulse voltage and the conductance of RS synapse, which can be used to integrate the weighted sum current. As an energy efficient memristor based spiking neural network, this combination of TS oscillatory neuron with RS synapse is further evaluated for image recognition achieving an accuracy of 79.2 2.4% for CIFAR 10 subset.
author_names: Qilin Hua

Huaqiang Wu

Bin Gao

Qingtian Zhang

Wei Wu

Yujia Li

Xiaohu Wang

Weiguo Hu

He Qian
title: Low Voltage Oscillatory Neurons for Memristor Based Neuromorphic Systems
venue: Global challenges
year: 2019

abstract: Abstract The pure circuit implementation of STDP is a worthwhile work. In this study, a novel spiking neuron circuit and STDP learning circuit based on memristors are designed. The proposed spiking neuron circuit, which is based on conventional leaky integrate and fire neuron and utilizes the nonlinear variation of memristance, is served to generate spikes. Different release thresholds and amplitudes of the spikes can be obtained through tuning circuit parameters. Extending on the spiking neuron circuit, a STDP learning circuit is built to perform unsupervised learning behaviors. Furthermore, a crossbar array architecture is finally considered to fabricate multi input multi output spiking neural network which also perform STDP learning effectively. Circuit simulation results in PSPICE prove the availability of the two proposed circuits.
author_names: Liang Zhao

Qinghui Hong

Xiaoping Wang
title: Novel designs of spiking neuron circuit and STDP learning circuit based on memristor
venue: Neurocomputing
year: 2018

abstract: This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate and fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically plausible learning mechanisms, such as spike timing dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike timing dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithmmanipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.
author_names: Xu Zhang

Greg Foderaro

Craig S Henriquez

Antonius M J VanDongen

Silvia Ferrari
title: A Radial Basis Function Spike Model for Indirect Learning via Integrate and Fire Sampling and Reconstruction Techniques
venue: Adv. Artif. Neural Syst.
year: 2012