\r\n\tAs the subject of adhesives is in constant development, this book's purpose is to get together information about adhesives science and technology, recent advances, and applications that use adhesive technology. Also, to make these contents available to engineering students, engineers, researchers, and the people interested in this topic. The book is expected to present works that aim to contribute to the development of new technologies and the use of non-traditional materials in engineering.
",isbn:"978-1-83880-670-5",printIsbn:"978-1-83880-669-9",pdfIsbn:"978-1-83880-671-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"c58b7d4c17e2a202af1dc4b906b7becb",bookSignature:"Prof. António Bastos Pereira and Dr. Alexandre Luiz Pereira",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11819.jpg",keywords:"The Technology of the Adhesives, Recent Advances, New Perspectives, Structural Adhesives Bonding, Durability of Structural Adhesives, New Applications, Repair of Composites, Bonding of Composites, Experimental Mechanics Tests, Thermal Analysis, Finite Element Method, Numerical Analysis.",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 15th 2022",dateEndSecondStepPublish:"May 13th 2022",dateEndThirdStepPublish:"July 12th 2022",dateEndFourthStepPublish:"September 30th 2022",dateEndFifthStepPublish:"November 29th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"12 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. António Pereira is a professor and researcher, who graduated from the University of Porto, and gained experience as an engineer working at Renault, with an h-index of 23, and more than 1500 citations for 70 papers published in SCI journals.",coeditorOneBiosketch:"An active researcher in Solid Mechanics, Dr. Alexandre Luiz Pereira holds a degree in Mathematics from the State University of Rio de Janeiro, and a degree in Mechanical Engineering from the Fluminense Federal University in Brazil.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"211131",title:"Prof.",name:"António",middleName:"Bastos",surname:"Pereira",slug:"antonio-pereira",fullName:"António Pereira",profilePictureURL:"https://mts.intechopen.com/storage/users/211131/images/system/211131.png",biography:"Founding shareholder and Director of Martifer Group (ca. 3500 employees) (1990-1999) - was responsible for the planning and production of about 500 steel structures and industrial equipment with a total amount exceeding 100 million euros.\nAssistant Professor at the Department of Mechanical Engineering, University of Aveiro, since 2000. Board Member and Member of the Executive Committee at the Department of Mechanical Engineering, University of Aveiro (2011 – 2015), currently Director of TEMA - Centre for Mechanical Technology and Automation.\nHis main research area has been mechanics of composite materials, with particular emphasis on delamination fracture mechanics. He has published 44 papers in SCI journals and has delivered 30 presentations at international conferences. 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His main research areas focus on the study of materials technology, from structural and hybrid composites, hyperelastic materials, and adhesives joints.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444312",firstName:"Sara",lastName:"Tikel",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/444312/images/20015_n.jpg",email:"sara.t@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Churchill, Maja Dutour Sikirić, Božana Čolović and Helga Füredi Milhofer",coverURL:"https://cdn.intechopen.com/books/images_new/8812.jpg",editedByType:"Edited by",editors:[{id:"219335",title:"Dr.",name:"David",surname:"Churchill",slug:"david-churchill",fullName:"David Churchill"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"66197",title:"Magnetorheological Elastomers: Materials and Applications",doi:"10.5772/intechopen.85083",slug:"magnetorheological-elastomers-materials-and-applications",body:'\nMagnetorheological elastomers (MREs) are a type of soft particle-reinforced magneto-active rubber-like composite material, whose physical or mechanical properties can be altered upon the application of a magnetic field [1, 2, 3, 4, 5]. MREs can be usually prepared by mixing micron-sized magnetic particles into nonmagnetic rubber-like matrices. In the presence of a magnetic field, MREs exhibit a magnetorheological effect providing a field-dependent physical or mechanical property, for example, a controllable modulus, due to the sensitive response of the magnetic particles to the field. While the field is removed, MREs will reclaim their original, natural property. It is believed that the embryo of MREs is firstly reported by Rigbi and Jilken [1, 6] in 1983, although the discovery of the basic magnetorheological effect can be historically retrospected to the 1940s for magnetic fluid [7]. MREs can be regarded as a solid-state analog to magnetorheological fluids (MRFs) [8, 9, 10, 11, 12]. In general, MREs exhibit a unique field-dependent material property when exposed to a magnetic field, and can overcome major issues faced in MRFs, for example, the deposition of iron particles, sealing problems, and environmental contamination. Such advantages offer MREs great potential for designing intelligent devices to be used in various engineering fields, especially in fields that involve vibration reduction, isolation, and absorption [1, 13, 14, 15, 16]. Recently, the study of the sensing behavior of MREs explosively emerged, for example, for sensing mechanical and magnetic signals [17, 18, 19, 20, 21, 22].
\nFrom the first report of MREs with soft ferrite particles filling into natural rubber [6], the research of the materials and related preparing methods of MREs develops very quickly. Briefly speaking, MREs consist of three basic components: magnetic particles, nonmagnetic elastic matrices, and additives. For the magnetic particles, higher permeability, higher saturation magnetization, and lower remanent magnetization are highly desirable for obtaining stronger magnetic field-sensitive effect. At present, the micron-sized carbonyl iron powder invented by BASF in 1925 is widely used [23, 24]. For the elastic matrix, there are lots of polymeric rubbers, for example, natural rubber [25, 26], silicone rubber [27, 28, 29, 30, 31], poly dimethylsiloxane (PDMS) [32, 33, 34], etc., for consideration. According to need, high modulus or low modulus can be chosen, but that the magnetic particles can be locked in the matrix in the absence or presence of a magnetic field is a basic requirement. For the additives, they are determined according to the choice of the particles and the matrix, and silicone oil is usually used as an additive in the fabrication of MREs [1]. Depending on the choice of the matrix, the preparing methods of MREs are many and various, and a high-temperature or room-temperature vulcanization curing method is usually used. Attributed to applying a magnetic field in the curing process, MREs can be prepared with anisotropic particle-formed microstructure. This kind of MREs is called anisotropic MREs. When no field is applied during the curing process, prepared MREs have isotropic particle-formed microstructure and this kind of MREs is called isotropic MREs. It is worth mentioning that the properties of isotropic MREs can differ much from those of anisotropic MREs.
\nDue to magnetic field-sensitive response of the magnetic particles, the material properties of MREs can be altered by using a magnetic field. For MREs, the magnetorheological effect is defined as the ratio of the value increment of a property at a measured magnetic field to the initial value of that property at zero magnetic field. In most studies, the distinctive change of the storage or loss modulus of MREs is a common concern. The magnetorheological effect is characterized by the ratio of modulus increment \n
The magnetic field-dependent shear storage modulus of MREs (modified from Refs. [
In this chapter, the materials, the preparing methods, the analytical models, and the applications of MREs will be reviewed. In the following section (Section 2), the materials, i.e., the magnetic particles, the nonmagnetic matrix and the additives, as well as the preparing methods of MREs will be introduced. In Section 3, the microstructures of prepared MREs, the analytical models of MREs, and some typical macroscopic properties of MREs will be presented. The relationship between the microstructures and the macroscopic properties will be qualitatively discussed. Then the applications of MREs will be briefly introduced in Section 4. Finally, Section 5 will give a summary of this chapter.
\nTo the magnetic particles, higher permeability, higher saturation magnetization, and lower remanent magnetization are highly desirable for obtaining stronger magnetic field-sensitive effect. Among a variety of magnetic particle materials, micrometer-sized carbonyl iron (CI) powder is currently widely used as a magnetic particle for preparing MREs. For a quick recognition, Figure 2(a) shows an example of the macroscopic image of CI powder. It shows that the CI powder is a very fine powder material. Figure 2(b) and (c) shows the scanning electron microscopy (SEM) images of CI powder (Type CN, produced by BASF SE Inc.) with different magnifications. The diameter of this kind of CI powder is several micrometers.
\nImages of carbonyl iron powder. The upper subfigure (a) shows the macroscopic image [
In general, the size of the magnetic particles can range from several micrometers to hundreds of micrometers [47, 48]. Figure 3 gives the size distribution of CI powder from experimental test. The size distribution can be analytically modeled by a lognormal distribution model as the following equation (Eq. (1)) shows.
\nThe size distribution of carbonyl iron powder (Type CIP-CN) [
\n
in which \n
Beside the size distribution of CI powder, the magnetic property of CI powder draws more attention of researchers. Higher permeability, higher saturation magnetization, and less remanent magnetization of magnetic particles are always highly desirable for obtaining stronger magnetic field-sensitive effect. As is shown in Figure 4, CI powder shows very high permeability, saturation magnetization, and very little remanent magnetization. The value of saturation magnetization can reach more than 600 kA/m and there is little remanent magnetization when magnetic field is removed. This mainly results from the fact that the content of Fe element in CI powder is usually more than 97.5% in weight fraction. Attributing to the excellent magnetic property, CI powder is widely used for fabricating materials including but not limited to MREs.
\nThe magnetic hysteresis loop of carbonyl iron powder (Type CIP-CN) at different temperatures [
A basic requirement of elastic matrices for fabricating MREs is that the matrices have soft elastic property, meaning that the matrices can stably hold the magnetic particles under no magnetic field and have a finite deformation under a magnetic field. For the elastic matrices, there are lots of polymeric rubbers that can be considered as candidates, for example, silicone rubber [50], natural rubber [51], butadiene rubber [52], butyl rubber [53], polyurethane [54], polydimethylsiloxane [55], epoxy [56], etc. For instantly having a basic recognition of the matrices, the following Figure 5 gives some examples of image and applications of widely used silicone rubber.
\nThe images of bulk material and related daily applications of silicone rubber [
The modulus of these matrices differs much from each other. For example, under normal conditions, the modulus of silicone rubber can be lower than 1.0 MPa [27]. That of natural rubber often reaches several MPa [25]. The shear modulus of PDMS varies with preparation conditions, but is typically in the range of 0.1–3.0 MPa [55]. The modulus of polyurethane can range from 0.01 MPa to several hundred MPa, attributed to its raw materials from fluid-like to solid-like [54]. Among a large amount of rubbers, silicone rubber compounds have characteristics of both inorganic and organic materials, and offer a number of advantages not found in other organic rubbers. From Figure 5, one can know that silicone rubbers have mechanically low modulus and good chemical stability and are nontoxic, nonpolluting, and human-body-friendly in daily use. As is shown in Figure 6, compared to the modulus of other rubbers, the modulus of silicone rubber is much lower within a large range of temperature. Besides, the thermal conductivity of silicone rubber can vary in a wide range. Based on the above-mentioned properties, silicone rubber can be chosen as an ideal soft elastic matrix for preparing MREs and is widely used in fabricating MREs. The other rubber matrices, with some unique mechanical or physical properties for special usage, can also been used in fabricating MREs according to need.
\nThe thermal conductivity and the modulus of silicone rubber (produced by Shin-Etsu Chemical Co., Ltd. Japan) [
Besides the magnetic particles and the elastic matrices, additives are also key components for preparing MREs. Silicone oil is usually used as an additive in material fabrication of MREs. When the molecules of the silicone oil enter the matrix, the gaps between the matrix molecules are increased, and the conglutination of the molecules is decreased. Apart from increasing the plasticity and fluidity of the matrix, the additives can average the distribution of the internal stress in the materials, which makes a stable material property for MR elastomer materials [1, 58]. The other additives include but are not limited to carbon black [59, 60, 61], carbon nanotubes [62, 63, 64, 65], silver nanowire [20], Rochelle salt [30], gamma-ferrite additives [66], etc.
\nA simplified illustration of the processing for preparing MREs is shown in Figure 7. Usually, the magnetic particles and the matrix are mechanically mixed with some additives into a mixture. The mixture has a very low yield stress, meaning that the mixture can easily deform and usually creep with itself. Then the mixture vulcanizes at room temperature (called room-temperature vulcanizing, RTV [4]) or high temperature (called high-temperature vulcanizing, HTV [45]) higher than 120°C. During the vulcanizing, in case of applying a magnetic field, the magnetic particles can move in the matrix and gradually aggregate forming chain-like structures along the direction of the field. After the magnetic field-assisted curing, anisotropic MREs, meaning that the magnetic particles form chain-like microstructures in MREs, can be prepared. When curing with no magnetic field, the magnetic particles will disperse uniformly in the matrix after vulcanization and thus isotropic MREs are prepared. Figure 8 shows some images of prepared MRE samples.
\nIllustration of the processing for preparing MREs. The magnetic particles and the matrix are mixed with additives into a mixture. When the mixture is cured with no external magnetic field, the mixture will be cured into isotropic MREs. However, in the case of the mixture curing under a uniform magnetic field, the mixture will be cured into anisotropic MREs.
Prepared cylindrical (a) and block (b) MREs with different thicknesses from 6.35 to 2.54 cm [
As a basic concern in the research of MREs, there are lots of studies focusing on the microstructures of MREs. Almost every report about a newly prepared MRE will show its microstructure. Figure 9 gives some typical SEM images with different times of magnification of the microstructures of carbonyl iron powder-embedded, natural rubber-based MREs samples. The volume fractions of iron particles for all samples are 11%. Figure 9(a) shows the images of a MRE sample cured with no magnetic field. It shows that the carbonyl iron particles randomly and uniformly disperse in the matrix. These two images are typical images showing the microstructures of isotropic MREs. The other images, i.e. Figure 9(b–f), show the microstructures of anisotropic MREs cured with magnetic field. As is shown, the magnetic particles will aggregate forming chain-like microstructures. The stronger the magnetic field intensity is when curing, the longer and thicker the magnetic particle-formed chains, as the magnetic interaction of neighbor particles is stronger. When the magnetic field is not strong enough, for example, 200 mT, the magnetic particles in the matrix can only move within a small width of range, resulting in that the magnetic particles can only form some short chain-like microstructures. The spaces between these chains are small. With the enhancement of the magnetic field during curing, the spaces will get wider and the anisotropy of MREs will get higher, implying that the properties of MREs will get more anisotropic.
\nSEM images with 200 times (left series of subfigures) and 1600 times (right series of subfigures) magnification of MREs prepared under a magnetic flux intensity B of (a) 0 mT, (b) 200 mT, (c) 400 mT, (d) 600 mT, (e) 800 mT, and (f) 1000 mT [
In recent years, the tendency to use quantitative methods instead of a qualitative analysis for structural investigation of the structure of nano- and microstructures has been increasing. Tomographic data open the possibility to achieve detailed quantitative data using techniques of digital image processing [69, 70]. To experimentally study the three-dimensional (3D) microstructures of MREs, Borin’s group [71, 72] firstly used the X-ray micro-computed tomography (X
Balasoiu et al. [70] allowed a detailed structural analysis of both isotropic MREs and anisotropic MREs. Figure 10 shows the exemplary X
Exemplary X
Magnetorheological elastomer: (a) part of the reconstructed image of a MRE sample and (b) extracted columns from the reconstructed tomography image. The directions of magnetic field and gravity are downward.
The study of the microstructure-based analytical model of MREs is always a key work for deeply knowing about MREs. Some pioneering works had been done studying the model of MREs in the late 1990s [74, 75] and a single-chain model was proposed assuming that the MREs are fully filled with single chains (Figure 12(a)). From these studies, it was reported that the optimum particle volume fraction for the largest fractional change in modulus at saturation is predicted to be 27%. Calculations of the zero-field shear modulus perpendicular to the chain axis indicate that it does not exceed the modulus of a filled elastomer with randomly dispersed particles of the same concentration. In 2010, Li and Zhang proposed bimodal particle-based chain-model of MREs (Figure 12(b)) [76]. In their work, theoretical and experimental studies of the mechanical performance and magnetorheological effects of MREs fabricated with mixtures of large and small particles were performed and an effective permeability model was developed to theoretically analyze the MR effect of bimodal particle-based MR elastomers. Six years later, a composite chain model (Figure 12(c)) was proposed [77, 78]. In this work, a particle chain composed of multiple kinds of particles with different sizes of diameter was introduced and a modeling strategy which accounts for elastic constituents and a nonlinear magnetization behavior of the particles is pursued. Most recently, a 3D multimodal chain model [79] has been proposed. In this model, magnetic particles with log-normal size distribution of diameter were introduced as fillers in soft elastic matrix. At the same time, a finite element model was built according to this model (Figure 12(d)). With the finite element model, one can computationally study the macroscopic physical or mechanical properties of MREs. In addition, as a basic issue, the study of the interaction between two magnetic particles still keeps developing [80, 81, 82]. Moreover, besides the microstructure-based analytical model of MREs, the phenomenological continuous medium-based models were also studied [83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93]. These works focus on theoretically and/or experimentally studying the magneto-viscoelastic models for MREs.
\nThe evolution of the model of MREs from (a) single chain model [
The most important characterization of MREs is that their macroscopic physical or mechanical properties can be altered upon the application of a magnetic field. For a long time, most studies have focused on the magnetic field-induced changes of the modulus or damping of MREs [1]. The shear storage/loss modulus or damping property of MREs can be measured by dynamic mechanical analyzer (DMA) or rheometer. As examples, Figure 13 shows the magnetic field-dependent shear storage modulus and damping of MRE samples. For some natural rubber-based MRE samples with different weight fraction of carbonyl iron particles, one can find that the magnetic field-induced change of their shear storage modulus can reach near or above two times of their initial magnitude. Moreover, as a characterizing of the damping property of MREs, the relationship between shear stress and shear strain, under various magnetic field strengths of silicone rubber-based MRE, is shown in Figure 13 (right). The results show that such MREs have controllable damping properties. The increase of the stress-strain loop area with magnetic field demonstrates that the damping capacity of MREs is a function of applied magnetic field. These field-dependent mechanical properties make MREs much promising in many engineering fields, especially in vibration reduction.
\nLeft: magnetic field-dependent shear storage modulus of different natural rubber-based MRE samples with 60, 70, 80, and 90% of carbonyl iron powder in weight fraction [
Magnetostriction of MREs is also a key concern when studying MREs [75, 95, 96, 97]. Figure 14 shows the simulated magnetostriction in MRE with different volume fractions of structured particle distributions. It shows that the magnetostriction of MREs is magnetic field-dependent. The intenser the field is and the higher the volume fraction of magnetic particle is, the larger the magnetostriction is. Further, full-field magnetostriction/deformation of a silicone rubber-based MRE under uniform magnetic field has been studied [98]. It shows that both isolated particles and grouped particles result in the concave-convex deformation of the MRE sample. Recently, the magnetic field-dependent electrical conductivity of MREs was emergently studied [99, 100, 101, 102, 103, 104, 105]. These field-dependent properties make MREs much promising in actuating and sensing.
\nSimulated magnetostriction in MRE with structured particle distributions: (a) effective magnetostrictive strains in the case of the particle chain being parallel to an applied magnetic field and (b) results for particle chain being perpendicular to an applied magnetic field. [
In 1993, Kordonsky pointed out that magnetorheological effect can be a base of new devices and technologies [106]. Years later, Carlson and Jolly gave an introduction of magnetorheological devices [3]. By possessing variable physical or mechanical properties when subjected to a magnetic field, MREs are natural candidates to be developed in many applications. In 2014, Li et al. [1] presented a state-of-the-art review on MRE-based devices. From this review, one can find that MREs can be used in many devices including but not limited to vibration absorbers, vibration isolators, sensors, controllable valves, and adaptive beam structures.
\nGinder et al. [107] firstly constructed a simple one-degree-of-freedom mass-spring system—an adaptive tuned vibration absorber—that utilizes MREs as variable-spring-rate elements. After that, the research on vibration MRE-based absorbers developed quickly (e.g., Refs. [13, 108, 109, 110, 111, 112]). Figure 15 shows the sketch of a designed MRE-based vibration absorber composed of a semi-active vibration absorption unit and a passive vibration isolation unit. The vibration absorption unit is composed of a magnetic conductor, a shearing sleeve, a bobbin core, an electromagnetic coil winding, and a circular cylindrical MRE vulcanized between the shearing sleeve and the bobbin core. The magnetic conductor, the bobbin core, and the electromagnetic coil are supported on the shearing sleeve through the MRE. The magnetic conductor and the bobbin core are connected by a bolt, and the shearing sleeve is fixed to the lower housing. The outer surface of the shearing sleeve is in clearance fit with the inner surface of the magnetic conductor, and the magnetic conductor can move vertically along the shearing sleeve. The MRE works in pure shear mode, and the magnetic conductor, the bobbin core, and the electromagnetic coil form the dynamic mass of the MRE-based vibration absorber together. The proposed MRE-based vibration absorber can absorb the vibration energy and thus reduce vibration.
\nThe 3D drawing (left) and the schematic representation (right) of a MRE-based dynamic vibration absorber [
Vibration isolators are devices which can isolate an object, such as a piece of equipment, from the source of vibration. Vibration isolators can be categorized into two groups: base isolation and force isolation, and the isolating modes have active and passive vibration isolation [113]. In Ref. [1], Li et al. had given a review on the application of vibration isolators for mechanical engineering and civil engineering. There are many works that focused on the study of MRE-based vibration isolators, for example, Refs. [16, 91, 92, 114, 115, 116, 117, 118]. Figure 16 shows an example of the designed layout and prototype of MRE-based isolator working in squeeze/elongation-shear mode. It shows that the initial vertical stiffness and damping coefficient of the magnetorheological elastomer isolator are 1.14 × 106 N/m and 495.8 N·s/m, respectively. The relative increase in stiffness and damping is 66.57% and 45.55%, respectively. Due to the properties of controllable stiffness and damping of MRE, the isolation transmissibility and root mean square of acceleration response can be reduced by 41.2% and 65.3%, respectively. The proposed MRE isolator can be used as a controllable stiffness device and has great potential in the field of vibration suppression for heavy equipment. [116].
\nMRE-based isolator: cross-sectional view of designed layout (left) and fabricated prototype (right) [
In addition to the field-sensitive elastic property, MREs possess several functions such as magnetoelasticity, magnetoresistance, magnetostriction, piezoresistance, and thermoresistance [1, 119]. The reasons for these functions are the changes in the spacing between the magnetic particles due to external loadings, which produce variations of the physical or mechanical properties of the MRE materials. Based on their field-sensitive properties, MREs have been developed for use as sensors and actuators, for example, force sensor [120], magnetoresistive sensor [18], magneto-sensitive strain sensor [20], flexible tri-axis tactile sensor [21], self-powered tribo-sensor [22], combined magnetic and mechanical sensor [17, 121], soft actuator [122], actuators for valves [123], MEMS magnetometer [124], etc. Moreover, the microwave response [125, 126] and 3D printing properties of MREs [127, 128, 129] have also been recently reported. It is worth being pointed out that the application of MREs is explosively developing.
\nIn this chapter, the materials and applications of MREs are briefly reviewed. Firstly, raw materials, including the magnetic particles, the rubber-like matrices, and the additives, are introduced. As the kind of the raw materials is getting more and more inconstant, there are a variety of raw materials that can be used to prepare MREs. Attributing to the variety of the raw materials, the kinds of prepared MREs are much various and the study on the MRE materials is a long-lasting discovery or inventive subject. Meanwhile, the X
The support of the National Natural Science Foundation of China (Grant Nos. 11602242 and 11502256) is acknowledged. The authors are grateful for the helpful suggestions of Prof. Xinglong Gong at the University of Science and Technology of China and the support of the colleagues who provided the original figures for the article.
\nThere is no conflict of interest.
Nature has provided innumerable examples of very efficient solutions to complex problems with seemingly simple rules. With these as inspiration, many engineering problems are tackled using bioinspired techniques. A few of bioinspired techniques are evolutionary and genetic algorithms, stigmergy, hidden Markov models, belief networks, neural networks, etc. These are applicable in a wide variety of domains from robotics [1], communication systems, routing [2], building construction [3], scheduling, optimization, machine intelligence, etc. The brain is a very efficient computing element capable of performing complex tasks. This is possible due to massively parallel computation being performed by the vast number of cells called neurons in the brain while consuming very little energy. This has inspired a domain of algorithms and techniques called artificial intelligence (AI) where machines are programmed to learn and then solve complex tasks. The recent advances in high performance computing and theoretical advances into statistical learning methodologies have enabled a widespread use of AI techniques for tasks such as pattern recognition, natural language understanding, speech recognition, computer vision, odor recognition, machine translation, medical diagnosis, gaming, autonomous driving, path planning, autonomous robots, financial market modeling and the list goes on. Solving these kinds of problems with efficiency is not possible with the traditional computing paradigms. These algorithms are mimicking biology or are inspired from biology to tackle the above problems. For example, it is not humanly possible to have traditional software program coded to classify an image of a simple object such as a cup with reasonable accuracy, considering the innumerable variations available in terms of shape, size, color, etc. However, this is a trivial task for a human being as our brains learn to identify the salient features of an object. The inner working of the brains, especially the way it processes information is the inspiration behind a class of AI techniques called neural networks.
\nAI requires a large amount of compute power while churning through massive amounts of data. Today’s real-world tasks require different sets of AI models with different modalities to interact with each other, hence needing a large pipeline with complex data dependencies. Training is time-consuming, while needing efficient multi-accelerator parallelization. Even with such advances we are nowhere close to the compute power or the efficiency of a human brain. Human brain is still a mystery and is a very actively researched topic. Several neuron models are proposed to mimic various aspects of how the brain works with the limited understand we have up till now.
\nSpiking neural networks (SNNs) are networks made up of interconnected computing elements called neurons. SNNs try to mimic biology to incorporate the efficiencies found in nature. These neurons use spikes to communicate with each other. SNNs are third generation of neural networks [4] and are gaining popularity due to its potential for very low energy dissipation due to their event-driven and asynchronous operation. SNNs are also interesting because of their ability learn in a distributed way using a technique called Spike Timing Dependent Plasticity (STDP) learning [5]. STDP relies on sparsely encoded spiking information among local neurons. SNNs are capable of learning rich spatio-temporal information [6]. In principle, SNNs can be fault tolerant due to its ability to re-learn and adapt the connections with other neurons, akin to how the brains learn. Also SNNs can natively interface with specialized hardware sensors which mimic biological vision (Dynamic Vision Sensor) and hearing (Dynamic Audio Sensor) [7] as they directly transduce sensory information to spikes.
\nIn the rest of the chapter, a brief introduction on neuron biology and artificial neuron models is presented, followed by discussion on information representation as spikes, different learning methodologies, tools, and platforms available for simulating and implementing SNNs and finally few case studies as examples of SNN usage.
\nIn this section, a brief overview of the biological neuron processes is provided to understand the inference and learning dynamics of SNNs. A few popular neuron models are discussed at a high level to make the reader aware of the diversity of such research and its use in SNNs.
\nComplex living organisms have specialized cells called neurons, which are the fundamental unit of central nervous system. Neurons can transmit and receive signals in the form of electrical impulses. In a human brain, there are an estimated 200 billion neurons. Also, there are several different types of neurons in the body. In general, a neuron consists of a cell body or soma consisting of cell machinery, nucleus, dendrites, and an axon as shown in \nFigure 1\n.
\nNeurons (by unknown author, licensed under CC BY-SA
The dendrites receive information from other neurons, and this causes a voltage buildup on the cell body. When this membrane potential reaches a certain threshold, an electrical impulse is generated, and the axon transmits this spike away from the cell body to other neurons. After a spike is generated, the neuron returns to a lower potential called resting potential. Also, immediately after a spike is generated, the neuron cannot generate another spike for a short duration called the refractory period. The axon terminates at axon terminals which interface with dendrites of other neurons; this is called as a synapse. A synapse is connection between a pre synaptic neuron (which generates electrical impulse) and a postsynaptic neuron (receives the spike information) as shown in \nFigure 1\n. The synapse is not a direct connection, instead it consists of a gap called synaptic cleft as shown in \nFigure 2\n. Discussion about astrocyte cells is presented later in Section 4.5.
\nNeuronal synapse along with astrocyte cells (author created).
When an electrical impulse reaches the synapse, the presynaptic neuron releases certain chemicals called neurotransmitters into the synaptic cleft. The postsynaptic neuron picks up these neurotransmitters eventually causing the postsynaptic neurons membrane potential to either increase or decrease. The brain learns by strengthening or weakening the existing synaptic connections or by making new synaptic connections or dissolving those which are no longer needed. In this way, the synapses make the brain plastic and provide the ability to learn. Also, the strength of the synapse also matters for learning as it can modulate the amount of neurotransmitters released in the synaptic cleft resulting in a stronger or weaker synapse and depending on the type of neurotransmitters released, the synapse can be excitatory or inhibitory. An excitatory synapse is one which would increase the membrane potential of the post synaptic neuron; conversely, an inhibitory synapse would decrease the membrane potential. Based on these fundamental concepts, several researchers have proposed various neuron models over the decades. We do not yet fully understand the inner workings of brains and is still an active field of research. New neuron models are being proposed frequently as our understanding of biology increases. A few neuron models are listed below, followed by an overview of select models.
\nSome of the models proposed try to mimic biology for the purpose of understanding and modeling neuro-physiological processes and some models more oriented toward computing purposes. A few of neuron models to consider are McCulloch and Pitts [8], Hodgkin-Huxley [9], Perceptron [10], Izhikevich [11] Integrate and fire [12], Leaky integrate-and-fire [13], Quadratic integrate-and-fire [14], Exponential integrate-and-fire [15], Generalized integrate-and-fire [16], Time-varying integrate-and-fire model [17], Integrate-and-fire or burst [18], Resonate-and-fire [19], and Bayesian neuron model [20].
\nHodgkin and Huxley [9] studied the giant axon of the squid and found currents induced by different types of ions namely sodium ions, potassium ions, and leakage current due to calcium ions. The cell consists of voltage-dependent ion channels which regulate the concentration of these ions across the cell membrane. For the sake of simplicity, at a high level, the total membrane current is the sum of current induced by membrane capacitance and the ion channel currents as shown in Eq. (1), where \n
They also describe gating variables to control the ion channels and the resting potential of the cell. When the membrane potential increases significantly above the resting potential, the gating variable activates and then deactivates the channels resulting in a spike. This is a very simplified model and has several limitations [21].
\nIzhikevich neuron model [11] is more biologically plausible as shown in equations below.
\nWhere \n
Izhikevich neuron model [
Over time, if a biological neuron does not spike, then any potential builtup would dissipate. This phenomenon is modeled by several variations of Leaky Integrate and Fire (LIF) models. LIF neuron model is very popular due to its ease of implementation as a software model and for developing dedicated hardware models. Digital hardware implementation is more popular than the analog variants, again due to its simplicity of design, fabrication, and scalability.
\nA typical generic LIF model adapted for discrete implementation [22] is represented as:
\nSynaptic integration
\nLeak integration
\nThreshold, fire and reset
\nWhere \n
Bayesian neuron (BN) model is proposed in [20]. BN model is a stochastic neuron model. When the membrane potential reaches the threshold a BN model fires a spike stochastically. It generates a spike based on a Poisson process where neuron \n
Where the weight of the synapse between
To generate a Poisson process with time-varying rate \n
\n\n
SNNs understand the language of spikes, and it is necessary to decide what is the best possible way to represent real-world data to achieve best possible training of the network and efficient inference. Different coding techniques model different aspects of input spectrum. Some of the spike coding techniques are described below to get an intuition of signal representation using spikes.
\nWith rate coded spike trains, the information is encoded in the number of spikes over a specified temporal window. The firing rate \n
Evidence of rate coding is experimentally shown in sensory and motor systems [24]. The number of spikes emitted by the receptor neuron increases with the force applied to the muscle.
\nIf the rate \n
Therefore, the instantaneous firing rate is
\nThe expected number of spikes for the temporal window \n
To summarize, the experimental procedure of counting spikes over a time \n
The Peri-stimulus-time histogram and the average time-dependent firing rate [
A spike train \n
The instantaneous firing rate is the expectation over trials.
\nAn empirical estimate of the instantaneous firing rate can be deduced as shown in Eq. (18). It implies that the PSTH as described above represents the instantaneous firing rate.
\nThe average firing rate can be computed for a single neuron, or for a population of neurons representing a class over a single run or over several trials. Rate coding over a time window is suitable for representing the strength of stimulation. On the other hand, population-based rate coding could convey the same information by employing several neurons in a shorter temporal window. The latter trades quick response over a number of neurons. There is evidence of Purkinje neurons demonstrating information coding which is not just firing rate but also the timing and duration of nonfiring, quiescent periods [25, 26].
\nIf the time of spike occurrence in a temporal window carries information, then such coding is referred to as temporal coding. In such coding schemes the quiescent periods and the spiking time both carry information. There are several evidences in biology demonstrating this behavior [27, 28]. A typical temporal code is shown in \nFigure 5A\n, where the time interval of spike to start of stimulus caries information. These are sometimes referred to as pulse codes. Another variation is Rank Order Coding, which uses the relative timing of spikes across a population of cells. Rank order codes look at time to spike across the neuron population and a rank order can be implied from the firing order among the neurons in the population as described in \nFigure 5B\n.
\nDifferent strategies for information coding with spikes (refer to [
There is evidence suggesting that simple temporal averaging of firing rate is too simplistic to model neuronal circuits in the brain [30]. To address some of the shortcomings, several derivations of coding schemes based on different combinations of above concepts are widely used. Few of the common schemes and some task specific coding schemes are Rate code, Time to spike code, Time-to-first-spike: Latency code [31], Reverse time to spike code, Weighted spike code [32], Burst code [33], Population code, Population rate, Rank order code [34], Phase-of-firing code [35, 36], Place code [37], etc. \nFigure 5\n summarizes a few coding strategies. These coding schemes require appropriate algorithms for converting real-world data to spikes and vice versa. A few common conversion techniques are discussed in the next section.
\nSNNs understand the language of spikes; therefore, we must transform the real-world data to appropriate spike representation and subsequently transform the output spikes to real-world formats for human consumption. There are several encoding and decoding algorithms available to achieve this goal. Several heuristics are also employed. Some of the coding techniques mentioned above infer a specific coding/decoding scheme. Based on the nature of application (such as images, audio, video, financial data, user activity data), one must choose which is the best approach.
\nImage pixel values are binned and proportional firing rates are assigned to different neurons in the receptive fields for each pixel neuron, hence generating random process with rate coding [38]. Since spikes have no polarity positive and negative spike, subchannels can be used to represent richer encoding of data. In threshold-based schemes, a spike is generated when the input signal intensity crosses a threshold. Real numbers are compared against different thresholds, and positive and negative spikes are produced accordingly which are rate coded [39]. BSA algorithm for encoding and decoding [40] is used for modeling brain-machine interfaces and neurological processes in the brain. The work presented by the authors of [41] provides details on step-forward (SF), and moving-window (MW) encoding schemes. In SF scheme, a baseline \n
Hebb postulated that synaptic efficacy increases from a presynaptic neuron if it repeatedly assists the post synaptic neuron [42]. This forms the fundamentals of STDP rule for learning. STDP mimics biology where a synapse is strengthened when a presynaptic spike occurs before a post synaptic spike in close intervals, this is called Long-Term Potentiation (LTP). On the other hand, the synapse is weakened if the post synaptic neuron fires before the presynaptic neuron in close intervals. This is called as Long-Term Depression (LTD). In biology neurons are highly selective due to lateral inhibition. This allows for them to learn discriminatory and unique features in an unsupervised manner leading to an emergent Winner Take All (WTA) behavior. Apart from this the biological system demonstrates homeostasis to maintain overall stability. These are key principles in SNN modeling. There are several ways to achieve WTA and homeostasis behavior, some directly modify the neuron state, others use neural circuits. One such example with a scalable neural circuit [43] is shown in \nFigure 6\n. A WTA network consists of inhibitor neurons suppressing the activation of other lateral symbol neurons as shown in \nFigure 6(a)\n. To assist in homeostasis a normalization of the excitations of one neural circuit compared to others can be achieved using a Normalized Winner Take All (NWTA) network as shown in \nFigure 6(b). Where an upper limit (UL) neuron uniformly inhibits all symbol neurons if they are firing beyond a desirable high threshold. On the contrary if the symbol neurons are firing below a desired low threshold, then the lower limit (LL) neuron triggers an excitor (Ex) neuron to uniformly boost the firing rate of all symbol neurons. In this manner all independent neural circuits within an SNN fire in the dynamic range of excitations of the overall network. Both hard and soft WTA behavior can be achieved based on the amount of inhibition generated. In Hard WTA only one symbol neuron is active whereas in soft WTA more than one symbol neuron is active providing richer context.
\n(a) Winner take all network (b) normalized winner take all network [
SNNs can learn in both unsupervised and supervised modes. WTA concepts are essential part of unsupervised learning as the neuron with highest excitation inhibits the lateral neurons the strongest hence enabling it to preferentially pick up unique features. Unsupervised learning is possible by employing a teacher signal which excites the specific neurons to fire thereby allowing it to learn the features represented by the input signal. STDP based learning has its advantages of being able to model spatio-tempotal dynamics. Where the spatial component refers to localized activity/learning and temporal component refers to additional information representation by the spike intervals along the time axis. With the constant advances in SNN research, native STDP based rules are catching up to the more popular backpropagation-based learning methods used in Artificial Neural Networks (ANN). STDP lends itself for efficient localized and distributed learning, which is a huge advantage over other learning methods. Also SNNs can be adapted to model memories in the form of Long Short-Term Memory networks [39] which shows that recurrent learning behavior is also possible. The following sub-sections discus few learning rules used in training SNNs along with a brief introduced to backpropagation-based learning.
\nA classic STDP rule [44] is shown in \nFigure 7\n. The STDP curve tries to approximate experimentally observed behavior.
\nClassic STDP curve [
Here \n
Where \n
There are two broad categorizations of STDP rules, additive and multiplicative STDP [38]. Multiplicative rule tends to be more stable than additive rule. In additive rules the weight changes are independent of current weight and requires additional constraints to keep the values in operating bounds. These weight changes however produce bimodal distribution resulting in strong competition. In multiplicative rule presented in [38], the weight change is inversely proportional to the current weight making it inherently stable and resulting in a unimodal distribution. This distribution lacks synaptic competition which is desirable for learning discriminative features. For such rules, competition must be introduced in a different method. The stable multiplicative rule is further explored below and simplified for efficient implementation. Here the STDP rule is modeled such that weight change of a synapse has an exponential dependence on its current weight as shown in \nFigure 8\n (a). Update for the weight \n
(a) Current weight vs weight change for learning rates (b) STDP windows (c) Comparison of Exp, 2P and Q2PS STDP rules [
If
\nthen,
\nIf
\nthen,
\nWhere \n
The Exp STDP rule requires an exponential and a multiplication operation for both LTP and LTD for each synapse. From the perspective of efficient digital hardware implementation these are expensive operations in terms of circuit area and computation time. Quantized 2-power shift rule (Q2PS), which approximates the Exp rule in Eq. (20) and Eq. (21) by removing both multiplication and exponential. The approximation is summarized in Eq. (22) and Eq. (23).
\nIf
\nIf
\nwhere \n
where \n
With the tremendous advances in the field of ANNs, a growing body of research is available on various statistical learning algorithms. ANNs are inspired by biology but they do not mimic it. ANNs are made up of artificial neuron models specifically tuned for compute purposes and model a biological neuron at a very abstract level. An artificial neuron computes weighted sum of input signals and then an activation function computes the neuron output. In these networks’ neurons transmit signals as real numbers. ANNs compute inference by transmitting the neuron signals in the forward direction. The learning happens usually via a method called Backpropagation. This algorithm computes the gradients based on the error signal produced by a cost function and propagates it back for each layer of neurons in the neural network. The weight updates are usually made using gradient descent algorithms. There are many flavors of gradient descent algorithms available in the literature. For back propagation to work the activation function must be differentiable. Unlike SNNs, where a spike is not differentiable. In general, ANNs have proven to be very effective in tackling a wide variety of problems. Using these algorithms as inspiration several modified STDP rules have been researched, one among them is discussed below. This overview is a very high-level introduction to some of the terminology required to understand the following section. The reader is encouraged to explore further on this topic.
\nThe Backpropagation-STDP (BP STDP) [45] algorithm uses the number of spikes in a spike trains as an approximation for the real value of an artificial neurons excitation. They also divide the time interval into sub-intervals such that each sub-interval contains zero or one spike.
\nIn supervised training, the weight adjustment is governed by the STDP model shown in Eq. (25) and Eq. (26), in conjunction with a teacher signal. The teacher signal when applied to target neurons undergo weight change based on STDP and non-target neurons undergo weight changes based on anti-STDP. Anti-STDP is the opposite of STDP where LTP and LTD equations are swapped. Target neurons are identified by spike trains with maximum spike frequency (\n
A target neuron would generate a spike \n
Stigmergy is a methodology where several independent agents produce an emergent behavior through indirect interaction among themselves. This is facilitated with the help of asynchronous communication through traces left in the environment by individual agents. Stigmergy has been observed in nature and widely researched upon especially in insect colonies, these principles have been applied towards solving various engineering problems. Recent advances in neuroscience have shown evidence of another type of cells called astrocytes working in tandem with neurons to regulate the behavior of the central nervous system [46]. Astrocytes are star shaped cells with several branches called as processes. The end of these branches called as end feet interface with a synapse by wrapping around it creating a region around the synaptic cleft called as microdomain as shown in \nFigure 2\n. Astrocytes also interface the neurons apart from the synapse providing a closed loop feedback mechanism. They also interface with other astrocytes like a synapse, instead this is called as a gap junction. Gap junction facilitates communication between astrocyte cells only through chemical means. Astrocytes are functionally very diverse and play a very important role, only a high-level concept with limited detail is introduced for understanding of relevant discussion. With the help of calcium ions as a signaling mechanism along with the help of neurotransmitters the astrocytes help regulate the efficiency of synaptic transmission. These cells play a critical role in maintaining homeostasis, modulating LTP, LTD and structural plasticity in the brain.
\nSpiking activity results in release of neurotransmitters and change in concentration among different ions in the microdomain and extra cellular space. These changes are monitored as traces for indirect communication by astrocytes. Astrocytes themselves behaving like an environment with calcium ion concentration gradients within the cell acting as a medium for other neuron agents to indirectly infer these changes. This interaction creates a feedback mechanism in an asynchronous and distributed manner [47]. \nFigure 9\n shows the emergent stigmergy pattern in the brain. Short term activity and long-term activity gets communicated over a distance to other synapses over a spatial domain. Greater the distance, lower would be the influence. The details about the stigmergy based brain plasticity is presented in [47], interested readers are encouraged to explore further. This is a relatively new discovery and extensive research is underway to understand the role of astrocytes in overall brain mechanics.
\nStigmergic interactions between astrocytes and neurons (modified from [
There are several spiking neural network simulation tools available which support biologically realistic neuron models for large scale networks. Some of the popular ones are:
\nBrian [48], is a free, open source simulator for spiking neural networks. This simulator is capable of running on several different platforms and is implemented in python making it extendable and easy to use.
\nNEST [49] is another simulator focusing on the dynamics, size and structure of neural systems both large and small. This tool is not intended for modeling the intricate biological details of a neuron.
\nNEURON [50] is simulation environment best suited for modeling individual neurons and their networks. This is popular among neuroscientists for its ability to handle complex models in a computationally efficient manner. Unlike above simulator, NEURON can handle morphological details of a neuron and is used to validate theoretical models with experimental data.
\nThe above tools are commonly used in modeling biologically realistic neuron modes. They have their own unique interfaces and low-level semantics. An effort is made to smooth things out with a tool independent API package developed on Python programming language called PyNN [51]. The PyNN framework provides API support to model SNNs at a high level of abstraction of all aspects of neuron modeling and SNN representation, including populations of neurons, connections, layers etc. Though this provides high level abstraction, it also provides the ability to program at a low level such as adjusting individual parameters at the neuron and synapse level. To make things easy PyNN provides a set of library implementation for neurons, synapses, STDP models etc. They also provide easy interfaces to model various connectivity patterns among neurons like; all-to-all, small-world, random distance-dependent etc. These APIs are simulator independent making the code portable across different supported simulation tools and neuromorphic hardware platforms. It is relatively straightforward to add support to any custom simulation tool. PyNN officially supports BRIAN, NEST and NEURON SNN simulation tools. It is also supported on SpiNNaker [52] and BrainScaleS-2 [53] neuromorphic hardware systems. There are several more simulation tools which work with PyNN.
\nCypress [54] is a C++ based SNN Simulation tool. This provides a C++ wrapper around PyNN APIs. Hence, extending the multi-platform reach of Cypress using C++ interface. It is also capable of executing networks remotely on neuromorphic compute platforms.
\nThe BrainScaleS-2 [53] is a mixed-signal accelerated neuromorphic system with analog neural core, digital connectivity along with embedded SIMD microprocessor. It is efficient for emulations of neurons, synapses, plasticity models etc. This hardware based system is capable of evaluating models up to ten thousand times faster than real time.
\nThe SpiNNaker [52] is another neuromorphic system custom built with digital multicore ARM processors. The SpiNNaker system (NM-MC-1) consists of custom chips each with eighteen cores sharing a local 128 MB RAM. The overall system scales to more than a million cores.
\nApart from the above tools and platforms the are many custom SNN tools available to model SNNs easily for machine learning purposes. ANNarchy (Artificial Neural Networks architect) [55] is a custom simulator for evaluating SNNs. This is implemented in C++ language, along with acceleration support provided using OpenMP/CUDA. The network definitions are provided using python interface.
\nNeuCube [6] is a development environment for creation of Brain-Like Artificial Intelligence. The computational architecture is suited for modeling SNN applications across several domain areas. This tool supports the latest neural network models for AI purpose. It supports PyNN interface, hence extending its versatility. This tool can run on CPU, GPU and SpiNNaker platforms, also a cloud version of the tool is available.
\nTrueNorth [56] is another neuromorphic platform capable of evaluating SNNs at faster than real time and at very low power. They demonstrate running state of the art neural networks on the hardware platform scaling up to 64 million neurons and 16 billion synapses while the system consumes only 70 W of power out of which only 15 W is consumed by the neuromorphic hardware components. The hardware supports inference only, with learning performed off chip.
\nLoihi [57] is the latest offering in the neuromorphic SNN hardware. This hardware approach gets rid of crossbar architecture, which is prevalent in most previous neuromorphic implementations, lending itself to greater amount of flexibility. Loihi is also capable of on-chip learning which is a huge advantage in terms of online learning of synapses.
\nOther simulators capable of modeling software based models and models for custom neuromorphic hardware are presented in [20, 58, 59, 60]. This is still an ongoing field of research and there are several more accelerator-based simulators available hence the reader is encouraged to explore further. Neuromorphic hardware using more exotic hardware devices like memristors and phase change memories are also an active area of research, they are yet to make it to mainstream consumption hence they are only mentioned here.
\nIn this section few case studies are presented to bolster the concepts discussed in this chapter. The topics covered here include STDP learning dynamics, probabilistic graphical models as SNNs, SNN with BP-STDP based learning and SNNs on Neuromorphic Hardware.
\nA SNN is trained [38] to classify handwritten digits from the MNIST dataset using the STDP based learning rules Exp, Q2PS and 2P presented in Section 4.2. The authors build a three-layer SNN as shown in \nFigure 10\n. The MNIST images are of 28x28 pixel dimensions, hence the input layer contains 784 neurons, one per image pixel. The second/hidden layer contains neurons for learning the features of the input images. The number of neurons in this layer is varied over different trials to evaluate the effectiveness of the learning rule. Finally, the third layer consists of 10 neurons for classifying the input with one neuron per class. The input layer encodes the pixel intensities with varying firing rate in the range of 0 Hz – 300 Hz. Each input neuron is fully connected to the hidden layer neurons similarly each hidden layer neuron is fully connected to the output/classification layer neurons. In this network all synapses are plastic with soft WTA connectivity implemented between input layer and hidden layer neurons to facilitate different neurons to pick up shared features. On the other hand, a hard WTA connectivity exists between hidden layer and the classification layer.
\nMNIST SNN architecture showing connectivity, input, learnt features, labels and t-SNE visualizations, along with accuracy results [
A qualitative analysis of the learning rule is depicted by the t-distributed stochastic neighbor embedding (t-SNE) [61] visualizations in \nFigure 10\n. The t-SNE algorithm maps high dimensional data points lying on different but related low-dimensional manifolds to lower dimensions by capturing local structure present in high dimensional data. The input layer firing rate visualizations show the clustering of digit classes in 2 dimensions based on raw pixel data which has 784 dimensions. Similarly, the second visualization is made using the firing rate based on the learnt features of hidden layer as input to the t-SNE algorithm with 100 dimensions. It can be clearly seen that the STDP rule produces tight clustering of input space which is projected on to the feature space. The classification layer further groups these features to its respective classes. Networks with different number of hidden layer neurons are experimented with and the results are shown in the bottom right side of \nFigure 10\n. The robustness of the learning method is also demonstrated with experiments yielding similar accuracies with additive Gaussian white noise along with the use of NWTA network.
\nAn inference network based on a probabilistic graphical model for sentence construction is created using Bayesian neurons. It consists of lexicons representing words and phrases. Here each lexicon is a WTA sub network.
\nThe network consists of two functional sections: word sub network and phrase sub network. Each symbol neuron in word sub network represents a possible word occurrence and each symbol neuron in phrase sub network represents a possible pair of words co-occurring. The synapses between the symbol neurons represent the log conditional probabilities of words and phrases co-occurring. This network is initialized to have same intrinsic potential across all symbol neurons resulting in same initial firing rate. Based on the synaptic weights the strongly connected neurons resonate and enhance each other while laterally inhibiting other symbol neurons within the lexicon WTA network. These winning neurons proportionally excite other symbol neurons across different lexicons. In this manner the network settles on a steady state firing rate which represents a contextually correct behavior. From each lexicon of the word sub network a symbol neuron is picked with highest firing rate representing a grammatically correct semantically meaningful sentence. The WTA connections in this network perform soft WTA action there by the facilitating the retention of contextual information. \nFigure 11\n (a) shows the network topology. For the experiments, random documents images are picked, and fuzzy character recognition is performed. Due to the fuzzy nature, each character position will result in several possible matches hence, multiple possible matches for each word position is possible as described in [62]. An example of lexicon set is [{we, wo, fe, fo, ne, no, ns, us} {must, musk, oust, onst, ahab, bust, chat} {now, noa, non, new, how, hew, hen, heu} {find, rind, tina} {the, fac, fro, kho} {other, ether}]. The SNN after evaluating the lexicons settles on a grammatically correct sentence as [we must now find the other] as seen in \nFigure 11\n (b).
\n(a) Sentence confabulation network, (b) confabulation results spike plot [
Using the learning rule presented in Section 4.4, the authors of [45] train SNNs to evaluate BP-STDP rule on the XOR problem, the iris dataset and the MNIST dataset. They show that the network can model the linearly inseparable XOR problem using an SNN with 2 input, 20 hidden and 2 output neurons. For the iris dataset they create a SNN with 4 input, 30 hidden and 3 output neurons. With this network they were able to achieve 96% accuracy which is comparable to ANN trained with traditional backpropagation with an accuracy of 96.7%. The SNN for MNIST dataset consists of 784 input neurons, 100 through 1500 hidden neurons and 10 output neurons. With this network they were able to achieve 97.2% classification accuracy.
\nDeep networks achieve higher accuracy in recognition tasks and in some cases outperform humans. Eedn framework is proposed in [63], which enables SNNs to be trained using backpropagation with batch normalization [64] and implement them on TrueNorth neuromorphic hardware. The Eedn trained networks are capable of achieving state-of-the-art accuracy across eight standard datasets of vision and speech. In this implementation the inference on hardware can be run at up to 2600 frames/s which is faster than real time while consuming very low power of at most 275 mW across their experiments. The network uses low precision ternary weights +1, 0 and − 1 for its synapses. A binary activation function with an approximate derivative is modeled to enable backpropagation. A hysteresis parameter is introduced in the weight update rule to avoid rapid oscillations of weights during learning. The input images are transduced by applying 12 different convolutional filter operators with binary outputs to get 12 channel input to the network as shown in \nFigure 12\n.
\nExample image from CIFAR10 (column 1) and the corresponding output of 12 typical transduction filters (columns 2–13) [
Experiments were performed on eight datasets using five different network sizes spanning across several TrueNorth chips. The results of the experiments are summarized in \nFigure 13\n.
\nAccuracy of different sized networks on eight datasets. For comparison, accuracy of state-of-the-art unconstrained approaches are shown as bold horizontal lines [
This chapter discussed several concepts and techniques, all of which are bio inspired. The case studies presented provide a strong basis to grasp the immense potential these algorithms provide in tackling the very complex problems of today, which were unimaginable without the advances in this field. This chapter specifically provided a beginner’s guide to the field of spiking neural networks. It presented a brief overview of neuron biology and notes on popular artificial neuron models. Information representation as spikes and how to transduce real world data to spikes and vice-versa was discussed which is similar to how brain represents information. Several tools for spiking neural network modeling and evaluation were provided for wholistic understanding and for experimental evaluation of one’s network models. A few case study examples are presented to understand the presented concepts and the scope of information presented in this chapter. This is an ongoing research and a very hot topic with substantially new concepts and discoveries being published every week. The motivation being the ability for machines to autonomously and efficiently perform tasks which were previously delegated to humans only along every aspect of our lives. This is a paradigm shift and research will continue to not only develop machine intelligence but also to understand the inner workings of our brains, our thoughts and advance the field of neuroscience.
\nThis chapter represents fundamental knowledge for understanding spiking neural networks. Some of the text and images are adopted from the available research literature. Rest of the work represents authors original contributions along with the co-authors of the following research contributions [20, 38, 39, 43, 60, 62]. I am thankful for the support of Dr. Qinru Qiu from Syracuse University and her research group members specifically Amar Shrestha in contributing during the original research.
\nartificial intelligence
\nartificial neural networks
\nBayesian neuron
\nbackpropagation-STDP
\nexcitor neuron
\nlower limit neuron
\nlong-term depression
\nlong-term potentiation
\nmoving-window
\nnormalized winner take all
\nperi-stimulus-time histogram
\nstep-forward
\nspiking neural network
\nspike timing dependent plasticity
\nt-distributed stochastic neighbor embedding
\nupper limit neuron
\nwinner take all
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Mans",coverURL:"https://cdn.intechopen.com/books/images_new/10926.jpg",editedByType:"Edited by",editors:[{id:"193905",title:"Dr.",name:"Dennis",middleName:"R. A.",surname:"R.A. Mans",slug:"dennis-r.a.-mans",fullName:"Dennis R.A. Mans"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"4",chapterContentType:"chapter",authoredCaption:"Authored by"}},{type:"book",id:"10334",title:"Dementia in Parkinson’s Disease",subtitle:"Everything you Need to Know",isOpenForSubmission:!1,hash:"3368e4a78022792496a7a727ac408f3a",slug:"dementia-in-parkinson-s-disease-everything-you-need-to-know",bookSignature:"Lin Zhang and John M. Olichney",coverURL:"https://cdn.intechopen.com/books/images_new/10334.jpg",editedByType:"Edited by",editors:[{id:"182291",title:"Prof.",name:"Lin",middleName:null,surname:"Zhang",slug:"lin-zhang",fullName:"Lin Zhang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:1816,seriesByTopicCollection:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],seriesByTopicTotal:3,mostCitedChapters:[{id:"19013",doi:"10.5772/21983",title:"Cell Responses to Surface and Architecture of Tissue Engineering Scaffolds",slug:"cell-responses-to-surface-and-architecture-of-tissue-engineering-scaffolds",totalDownloads:10450,totalCrossrefCites:128,totalDimensionsCites:296,abstract:null,book:{id:"314",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",title:"Regenerative Medicine and Tissue Engineering",fullTitle:"Regenerative Medicine and Tissue Engineering - Cells and Biomaterials"},signatures:"Hsin-I Chang and Yiwei Wang",authors:[{id:"45747",title:"Dr.",name:"Hsin-I",middleName:null,surname:"Chang",slug:"hsin-i-chang",fullName:"Hsin-I Chang"},{id:"53659",title:"Ms.",name:"Yiwei",middleName:null,surname:"Wang",slug:"yiwei-wang",fullName:"Yiwei Wang"}]},{id:"46479",doi:"10.5772/57353",title:"Floating Drug Delivery Systems for Eradication of Helicobacter pylori in Treatment of Peptic Ulcer Disease",slug:"floating-drug-delivery-systems-for-eradication-of-helicobacter-pylori-in-treatment-of-peptic-ulcer-d",totalDownloads:2822,totalCrossrefCites:135,totalDimensionsCites:287,abstract:null,book:{id:"3839",slug:"trends-in-helicobacter-pylori-infection",title:"Trends in Helicobacter pylori Infection",fullTitle:"Trends in Helicobacter pylori Infection"},signatures:"Yousef Javadzadeh and Sanaz Hamedeyazdan",authors:[{id:"94276",title:"Prof.",name:"Yousef",middleName:null,surname:"Javadzadeh",slug:"yousef-javadzadeh",fullName:"Yousef Javadzadeh"},{id:"98229",title:"Dr.",name:"Sanaz",middleName:null,surname:"Hamedeyazdan",slug:"sanaz-hamedeyazdan",fullName:"Sanaz Hamedeyazdan"}]},{id:"25512",doi:"10.5772/30872",title:"Epidemiology of Psychological Distress",slug:"epidemiology-of-psychological-distress",totalDownloads:8768,totalCrossrefCites:88,totalDimensionsCites:235,abstract:null,book:{id:"727",slug:"mental-illnesses-understanding-prediction-and-control",title:"Mental Illnesses",fullTitle:"Mental Illnesses - Understanding, Prediction and Control"},signatures:"Aline Drapeau, Alain Marchand and Dominic Beaulieu-Prévost",authors:[{id:"84582",title:"Dr.",name:"Aline",middleName:null,surname:"Drapeau",slug:"aline-drapeau",fullName:"Aline Drapeau"},{id:"84605",title:"Dr.",name:"Alain",middleName:null,surname:"Marchand",slug:"alain-marchand",fullName:"Alain Marchand"},{id:"84606",title:"Dr.",name:"Dominic",middleName:null,surname:"Beaulieu-Prévost",slug:"dominic-beaulieu-prevost",fullName:"Dominic Beaulieu-Prévost"}]},{id:"64762",doi:"10.5772/intechopen.82511",title:"Mechanism and Health Effects of Heavy Metal Toxicity in Humans",slug:"mechanism-and-health-effects-of-heavy-metal-toxicity-in-humans",totalDownloads:10111,totalCrossrefCites:91,totalDimensionsCites:216,abstract:"Several heavy metals are found naturally in the earth crust and are exploited for various industrial and economic purposes. Among these heavy metals, a few have direct or indirect impact on the human body. Some of these heavy metals such as copper, cobalt, iron, nickel, magnesium, molybdenum, chromium, selenium, manganese and zinc have functional roles which are essential for various diverse physiological and biochemical activities in the body. However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.",book:{id:"7111",slug:"poisoning-in-the-modern-world-new-tricks-for-an-old-dog-",title:"Poisoning in the Modern World",fullTitle:"Poisoning in the Modern World - New Tricks for an Old Dog?"},signatures:"Godwill Azeh Engwa, Paschaline Udoka Ferdinand, Friday Nweke Nwalo and Marian N. 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The traditional healer provides health care services based on culture, religious background, knowledge, attitudes, and beliefs that are prevalent in his community. Illness is regarded as having both natural and supernatural causes and thus must be treated by both physical and spiritual means, using divination, incantations, animal sacrifice, exorcism, and herbs. Herbal medicine is the cornerstone of traditional medicine but may include minerals and animal parts. The adjustment is ok, but may be replaced with –‘ Herbal medicine was once termed primitive by western medicine but through scientific investigations there is a better understanding of its therapeutic activities such that many pharmaceuticals have been modeled on phytochemicals derived from it. Major obstacles to the use of African medicinal plants are their poor quality control and safety. Traditional medical practices are still shrouded with much secrecy, with few reports or documentations of adverse reactions. However, the future of African traditional medicine is bright if viewed in the context of service provision, increase of health care coverage, economic potential, and poverty reduction. Formal recognition and integration of traditional medicine into conventional medicine will hold much promise for the future.",book:{id:"6302",slug:"herbal-medicine",title:"Herbal Medicine",fullTitle:"Herbal Medicine"},signatures:"Ezekwesili-Ofili Josephine Ozioma and Okaka Antoinette Nwamaka\nChinwe",authors:[{id:"191264",title:"Prof.",name:"Josephine",middleName:"Ozioma",surname:"Ezekwesili-Ofili",slug:"josephine-ezekwesili-ofili",fullName:"Josephine Ezekwesili-Ofili"},{id:"211585",title:"Prof.",name:"Antoinette",middleName:null,surname:"Okaka",slug:"antoinette-okaka",fullName:"Antoinette Okaka"}]},{id:"76640",title:"Control of Clinical Laboratory Errors by FMEA Model",slug:"control-of-clinical-laboratory-errors-by-fmea-model",totalDownloads:1112,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Patient safety is an aim for clinical applications and is a fundamental principle of healthcare and quality management. The main global health organizations have incorporated patient safety in their review of work practices. The data provided by the medical laboratories have a direct impact on patient safety and a fault in any of processes such as strategic, operational and support, could affect it. To provide appreciate and reliable data to the physicians, it is important to emphasize the need to design risk management plan in the laboratory. Failure Mode and Effect Analysis (FMEA) is an efficient technique for error detection and reduction. Technical Committee of the International Organization for Standardization (ISO) licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. FMEA model helps to identify quality failures, their effects and risks with their reduction/elimination, which depends on severity, probability and detection. Applying FMEA in clinical approaches can lead to a significant reduction of the risk priority number (RPN).",book:{id:"9808",slug:"contemporary-topics-in-patient-safety-volume-1",title:"Contemporary Topics in Patient Safety",fullTitle:"Contemporary Topics in Patient Safety - Volume 1"},signatures:"Hoda Sabati, Amin Mohsenzadeh and Nooshin Khelghati",authors:[{id:"340486",title:"M.Sc.",name:"Hoda",middleName:null,surname:"Sabati",slug:"hoda-sabati",fullName:"Hoda Sabati"},{id:"348872",title:"M.Sc.",name:"Amin",middleName:null,surname:"Mohsenzadeh",slug:"amin-mohsenzadeh",fullName:"Amin Mohsenzadeh"},{id:"348874",title:"MSc.",name:"Nooshin",middleName:null,surname:"Khelghati",slug:"nooshin-khelghati",fullName:"Nooshin Khelghati"}]},{id:"64762",title:"Mechanism and Health Effects of Heavy Metal Toxicity in Humans",slug:"mechanism-and-health-effects-of-heavy-metal-toxicity-in-humans",totalDownloads:10088,totalCrossrefCites:90,totalDimensionsCites:209,abstract:"Several heavy metals are found naturally in the earth crust and are exploited for various industrial and economic purposes. Among these heavy metals, a few have direct or indirect impact on the human body. Some of these heavy metals such as copper, cobalt, iron, nickel, magnesium, molybdenum, chromium, selenium, manganese and zinc have functional roles which are essential for various diverse physiological and biochemical activities in the body. However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.",book:{id:"7111",slug:"poisoning-in-the-modern-world-new-tricks-for-an-old-dog-",title:"Poisoning in the Modern World",fullTitle:"Poisoning in the Modern World - New Tricks for an Old Dog?"},signatures:"Godwill Azeh Engwa, Paschaline Udoka Ferdinand, Friday Nweke Nwalo and Marian N. Unachukwu",authors:[{id:"241837",title:"Mr.",name:"Godwill Azeh",middleName:null,surname:"Engwa",slug:"godwill-azeh-engwa",fullName:"Godwill Azeh Engwa"},{id:"274194",title:"BSc.",name:"Paschaline Ferdinand",middleName:null,surname:"Okeke",slug:"paschaline-ferdinand-okeke",fullName:"Paschaline Ferdinand Okeke"},{id:"286975",title:"Dr.",name:"Friday",middleName:null,surname:"Nweke Nwalo",slug:"friday-nweke-nwalo",fullName:"Friday Nweke Nwalo"},{id:"286976",title:"Dr.",name:"Marian",middleName:null,surname:"Unachukwu",slug:"marian-unachukwu",fullName:"Marian Unachukwu"}]},{id:"65467",title:"Anesthesia Management for Large-Volume Liposuction",slug:"anesthesia-management-for-large-volume-liposuction",totalDownloads:5710,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The apparent easiness with which liposuction is performed favors that patients, young surgeons, and anesthesiologists without experience in this field ignore the many events that occur during this procedure. Liposuction is a procedure to improve the body contour and not a surgery to reduce weight, although recently people who have failed in their plans to lose weight look at liposuction as a means to contour their body figure. Tumescent liposuction of large volumes requires a meticulous selection of each patient; their preoperative evaluation and perioperative management are essential to obtain the expected results. The various techniques of general anesthesia are the most recommended and should be monitored in the usual way, as well as monitoring the total doses of infiltrated local anesthetics to avoid systemic toxicity. The management of intravenous fluids is controversial, but the current trend is the restricted use of hydrosaline solutions. The most feared complications are deep vein thrombosis, pulmonary thromboembolism, fat embolism, lung edema, hypothermia, infections and even death. The adherence to the management guidelines and prophylaxis of venous thrombosis/thromboembolism is mandatory.",book:{id:"6221",slug:"anesthesia-topics-for-plastic-and-reconstructive-surgery",title:"Anesthesia Topics for Plastic and Reconstructive Surgery",fullTitle:"Anesthesia Topics for Plastic and Reconstructive Surgery"},signatures:"Sergio Granados-Tinajero, Carlos Buenrostro-Vásquez, Cecilia\nCárdenas-Maytorena and Marcela Contreras-López",authors:[{id:"273532",title:"Dr.",name:"Sergio Octavio",middleName:null,surname:"Granados Tinajero",slug:"sergio-octavio-granados-tinajero",fullName:"Sergio Octavio Granados Tinajero"}]},{id:"30178",title:"Chest Mobilization Techniques for Improving Ventilation and Gas Exchange in Chronic Lung Disease",slug:"chest-mobilization-techniques-for-improving-ventilation-and-gas-exchange-in-chronic-lung-disease",totalDownloads:30993,totalCrossrefCites:0,totalDimensionsCites:5,abstract:null,book:{id:"648",slug:"chronic-obstructive-pulmonary-disease-current-concepts-and-practice",title:"Chronic Obstructive Pulmonary Disease",fullTitle:"Chronic Obstructive Pulmonary Disease - Current Concepts and Practice"},signatures:"Donrawee Leelarungrayub",authors:[{id:"73709",title:"Associate Prof.",name:"Jirakrit",middleName:null,surname:"Leelarungrayub",slug:"jirakrit-leelarungrayub",fullName:"Jirakrit Leelarungrayub"}]}],onlineFirstChaptersFilter:{topicId:"3",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81721",title:"Atrial Fibrillation and Stroke",slug:"atrial-fibrillation-and-stroke",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.104619",abstract:"Atrial fibrillation (AF) represents a major cause of morbidity and mortality in adults, especially for its strong association with thromboembolism and stroke. In this chapter, we aim to provide an overview on this cardiac arrhythmia, addressing several important questions. Particularly, we faced the possible mechanisms leading to an increased risk of embolism in AF, emphasizing how Virchow’s triad for thrombogenesis is unable to fully explain this risk. Disentangling the risk of stroke caused by AF and by other associated vascular conditions is extremely challenging, and risk stratification of patients with AF into those at high and low risk of thromboembolism has become a crucial determinant of optimal antithrombotic prophylaxis. Moreover, we discuss the typical clinical and radiological characteristics of cardioembolic strokes, addressing acute, time-dependent reperfusional therapies in case of ischemic stroke. The role of anticoagulation in AF is also fully analyzed; the benefit of oral anticoagulation generally outweighs the risk of bleeding in AF patients, and a variety of scoring systems have been developed to improve clinical decision-making when initiating anticoagulation. With their predictable pharmacokinetic profiles, wide therapeutic windows, fewer drug–drug and drug-food interactions, and the non-vitamin K antagonist (VKA) oral anticoagulants (NOACs) have changed the landscape of thromboprophylaxis for AF patients, offering the opportunity to use effective anticoagulants without the need for intensive therapeutic drug monitoring.",book:{id:"10782",title:"Cerebrovascular Diseases - Elucidating Key Principles",coverURL:"https://cdn.intechopen.com/books/images_new/10782.jpg"},signatures:"Francesca Spagnolo, Vincenza Pinto and Augusto Maria Rini"},{id:"80743",title:"Air Quality and Health in West Africa",slug:"air-quality-and-health-in-west-africa",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.102706",abstract:"One of the most important elements for survival is air. Its significance cannot be overstated, necessitating proactive measures and regulations to ensure clean air in our atmosphere. Africa is one of the continents with the worst air quality. According to NASA modelling research, air pollution causes approximately 780,000 premature deaths per year in Africa. Experiments were carried out by the European-African consortium DACCIWA to investigate the causes and effects of air pollution by looking at the entire chain of natural and human-made emissions, from formation to dispersion to repercussions. The findings suggest that air pollution has already reached a dangerous threshold for human health in most West African countries. The aim of this chapter is to highlight and increase awareness about the severe risk that air pollution poses to the health of inhabitants of West African countries.",book:{id:"11231",title:"Air Quality and Health",coverURL:"https://cdn.intechopen.com/books/images_new/11231.jpg"},signatures:"Odubanjo D. Adedolapo"},{id:"81915",title:"Paracetamol-Induced Hepatotoxicity",slug:"paracetamol-induced-hepatotoxicity",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.104729",abstract:"Drug-induced hepatotoxicity is common in clinical settings, one of the commonly used drugs leading to liver injury is paracetamol. It is a commonly used analgesic and antipyretic drug. The toxicity of paracetamol has been described in accidental, iatrogenic, and intentional ingestion; also, the extent of liver injury varies from person to person depending on host factors, nutritional status, age, etc. The toxicity of paracetamol is not usually recognized by clinicians as initially, the symptoms are subtle. There is a specific antidote available for paracetamol-induced liver injury to prevent acute liver failure; however, it needs to be given time for proper action, therefore a strong clinical suspicion is to be taken when there is no proper history of ingestion.",book:{id:"11265",title:"Hepatotoxicity",coverURL:"https://cdn.intechopen.com/books/images_new/11265.jpg"},signatures:"Nida Mirza"},{id:"80928",title:"Trauma Resuscitation, Mass Casualty Incident Management and COVID 19: Experience from a South African Trauma Unit",slug:"trauma-resuscitation-mass-casualty-incident-management-and-covid-19-experience-from-a-south-african-",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.103971",abstract:"The COVID 19 pandemic has spanned 2 years and is still ongoing with many questions arising. We attempt to answer some pertinent questions with literature as well as anecdotal evidence from our facility. To describe any changes to the resuscitation of trauma patients during the COVID 19 pandemic if any. During the COVID 19 pandemic, Johannesburg a city in the Gauteng Province of South Africa experienced civil unrest and a fire at one of its trauma units, this resulted in a mass casualty incident (MCI) at the only functional trauma unit in the public sector. Results of this observational study will be elucidated. Focus is placed on PPE protocols, trauma resuscitations, MCI management, triage principles and the changing surgeon’s role within the pandemic.",book:{id:"11297",title:"ICU Management and Protocols",coverURL:"https://cdn.intechopen.com/books/images_new/11297.jpg"},signatures:"Naadiyah Laher"},{id:"81912",title:"Renal Replacement Therapies in the Intensive Care Unit",slug:"renal-replacement-therapies-in-the-intensive-care-unit",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105033",abstract:"Renal replacement therapies (RRT) are commonly used in critically ill patients to achieve solute clearance, maintain acid-base status, and remove fluid excess. The last two decades have seen the emergence of large randomized control trials bringing new evidence regarding how RRT should now be managed in the ICU. RRT is considered a vital supportive care and needs to be adequately prescribed and delivered. This chapter first summarizes the basic principles and characteristics of the three major RTT modalities: intermittent hemodialysis (IHD), prolonged intermittent RRT (PIRRT), and continuous RRT (CRRT). Then, the large body of literature regarding indications for initiation (early vs late), choice of modality (intermittent vs continuous and diffusion vs convection), dosing (intensive vs less-intensive), and anticoagulation alternatives is reviewed to guide clinical decision-making. Recent evidence in the optimal timing of discontinuing RRT is reported. Finally, troubleshooting scenarios frequently seen in clinics and requiring an adapted RRT prescription are also discussed.",book:{id:"11297",title:"ICU Management and Protocols",coverURL:"https://cdn.intechopen.com/books/images_new/11297.jpg"},signatures:"Dominic Godbout, Philippe Lachance and Jean-Maxime Côté"},{id:"81919",title:"A Role for Cardiac Glycosides in GBM Therapy",slug:"a-role-for-cardiac-glycosides-in-gbm-therapy",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105022",abstract:"There is a pressing need for new effective therapeutic strategies to treat glioblastoma (GBM). Cardiac glycoside compounds consisting of both cardenolides and bufadienolides have been shown to possess potent activity against GBM cell lines and in vivo GBM tumors. In addition, recent research has shown that certain cardiac glycoside compounds contribute to an additive and even synergistic manner with the standard of care GBM treatments such as radiotherapy and chemotherapy. Finally, the finding that cardiac glycosides may offer a unique role in the control of GBM stem cells offers hope for better therapeutic outcomes in treating this deadly form of brain cancer.",book:{id:"11597",title:"Glioblastoma - Current Evidences",coverURL:"https://cdn.intechopen.com/books/images_new/11597.jpg"},signatures:"Yuchen Du, Xiao-Nan Li, Peiying Yang and Robert A. Newman"}],onlineFirstChaptersTotal:775},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],testimonialsList:[]},series:{item:{},subseries:{},overviewPageOFChapters:[],overviewPagePublishedBooks:[],openForSubmissionBooks:{},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"news.list",path:"/news",hash:"",query:{},params:{},fullPath:"/news",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()