CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: artificial neuron</title> <meta name="description" content="Search results for: artificial neuron"> <meta name="keywords" content="artificial neuron"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="artificial neuron" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="artificial neuron"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2195</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: artificial neuron</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2195</span> Description of the Non-Iterative Learning Algorithm of Artificial Neuron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Akhmetov">B. S. Akhmetov</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20T.%20Akhmetova"> S. T. Akhmetova</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20I.%20Ivanov"> A. I. Ivanov</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20S.%20Kartbayev"> T. S. Kartbayev</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Y.%20Malygin"> A. Y. Malygin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neuron" title="artificial neuron">artificial neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrics" title=" biometrics"> biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrical%20applications" title=" biometrical applications"> biometrical applications</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20of%20neuron" title=" learning of neuron"> learning of neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=non-iterative%20algorithm" title=" non-iterative algorithm"> non-iterative algorithm</a> </p> <a href="https://publications.waset.org/abstracts/19446/description-of-the-non-iterative-learning-algorithm-of-artificial-neuron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19446.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2194</span> Stimulus-Dependent Polyrhythms of Central Pattern Generator Hardware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Le%20Zhao">Le Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Nogaret"> Alain Nogaret</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have built universal Central Pattern Generator (CPG) hardware by interconnecting Hodgkin-Huxley neurons with reciprocally inhibitory synapses. We investigate the dynamics of neuron oscillations as a function of the time delay between current steps applied to individual neurons. We demonstrate stimulus dependent switching between spiking polyrhythms and map the phase portraits of the neuron oscillations to reveal the basins of attraction of the system. We experimentally study the dependence of the attraction basins on the network parameters: the neuron response time and the strength of inhibitory connections. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20pattern%20generator" title="central pattern generator">central pattern generator</a>, <a href="https://publications.waset.org/abstracts/search?q=winnerless%20competition%20principle" title=" winnerless competition principle"> winnerless competition principle</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=synapses" title=" synapses"> synapses</a> </p> <a href="https://publications.waset.org/abstracts/5011/stimulus-dependent-polyrhythms-of-central-pattern-generator-hardware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2193</span> Neuron Dynamics of Single-Compartment Traub Model for Hardware Implementations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20C.%20Moctezuma">J. C. Moctezuma</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Bre%C3%B1a-Medina"> V. Breña-Medina</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Luis%20Nunez-Yanez"> Jose Luis Nunez-Yanez</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20P.%20McGeehan">Joseph P. McGeehan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we make a bifurcation analysis for a single compartment representation of Traub model, one of the most important conductance-based models. The analysis focus in two principal parameters: current and leakage conductance. Study of stable and unstable solutions are explored; also Hop-bifurcation and frequency interpretation when current varies is examined. This study allows having control of neuron dynamics and neuron response when these parameters change. Analysis like this is particularly important for several applications such as: tuning parameters in learning process, neuron excitability tests, measure bursting properties of the neuron, etc. Finally, a hardware implementation results were developed to corroborate these results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Traub%20model" title="Traub model">Traub model</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinsky-Rinzel%20model" title=" Pinsky-Rinzel model"> Pinsky-Rinzel model</a>, <a href="https://publications.waset.org/abstracts/search?q=Hopf%20bifurcation" title=" Hopf bifurcation"> Hopf bifurcation</a>, <a href="https://publications.waset.org/abstracts/search?q=single-compartment%20models" title=" single-compartment models"> single-compartment models</a>, <a href="https://publications.waset.org/abstracts/search?q=bifurcation%20analysis" title=" bifurcation analysis"> bifurcation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron%20modeling" title=" neuron modeling"> neuron modeling</a> </p> <a href="https://publications.waset.org/abstracts/10310/neuron-dynamics-of-single-compartment-traub-model-for-hardware-implementations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2192</span> Continuous Functions Modeling with Artificial Neural Network: An Improvement Technique to Feed the Input-Output Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Belayadi">A. Belayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mougari"> A. Mougari</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Ait-Gougam"> L. Ait-Gougam</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Mekideche-Chafa"> F. Mekideche-Chafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20computing" title="neural network computing">neural network computing</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20functions%20generating%20the%20input-output%20mapping" title=" continuous functions generating the input-output mapping"> continuous functions generating the input-output mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=decreasing%20the%20training%20time" title=" decreasing the training time"> decreasing the training time</a>, <a href="https://publications.waset.org/abstracts/search?q=machines%20with%20big%20memories" title=" machines with big memories"> machines with big memories</a> </p> <a href="https://publications.waset.org/abstracts/45427/continuous-functions-modeling-with-artificial-neural-network-an-improvement-technique-to-feed-the-input-output-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45427.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2191</span> Impact of Neuron with Two Dendrites in Heart Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20Selmi">Kaouther Selmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaeddine%20Sridi"> Alaeddine Sridi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Bouallegue"> Mohamed Bouallegue</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Bouallegue"> Kais Bouallegue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrites" title=" dendrites"> dendrites</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20behavior" title=" heart behavior"> heart behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a> </p> <a href="https://publications.waset.org/abstracts/170171/impact-of-neuron-with-two-dendrites-in-heart-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170171.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2190</span> Heterogeneous Intelligence Traders and Market Efficiency: New Evidence from Computational Approach in Artificial Stock Markets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yosra%20Mefteh%20Rekik">Yosra Mefteh Rekik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A computational agent-based model of financial markets stresses interactions and dynamics among a very diverse set of traders. The growing body of research in this area relies heavily on computational tools which by-pass the restrictions of an analytical method. The main goal of this research is to understand how the stock market operates and behaves how to invest in the stock market and to study traders’ behavior within the context of the artificial stock markets populated by heterogeneous agents. All agents are characterized by adaptive learning behavior represented by the Artificial Neuron Networks. By using agent-based simulations on artificial market, we show that the existence of heterogeneous agents can explain the price dynamics in the financial market. We investigate the relation between market diversity and market efficiency. Our empirical findings demonstrate that greater market heterogeneity play key roles in market efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agent-based%20modeling" title="agent-based modeling">agent-based modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20stock%20market" title=" artificial stock market"> artificial stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20expectations" title=" heterogeneous expectations"> heterogeneous expectations</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20stylized%20facts" title=" financial stylized facts"> financial stylized facts</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20finance" title=" computational finance"> computational finance</a> </p> <a href="https://publications.waset.org/abstracts/28310/heterogeneous-intelligence-traders-and-market-efficiency-new-evidence-from-computational-approach-in-artificial-stock-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2189</span> Learning from Dendrites: Improving the Point Neuron Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Vandesompele">Alexander Vandesompele</a>, <a href="https://publications.waset.org/abstracts/search?q=Joni%20Dambre"> Joni Dambre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dendritic%20computation" title="dendritic computation">dendritic computation</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking%20neural%20networks" title=" spiking neural networks"> spiking neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20neuron%20model" title=" point neuron model"> point neuron model</a> </p> <a href="https://publications.waset.org/abstracts/127988/learning-from-dendrites-improving-the-point-neuron-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2188</span> Comparison of Spiking Neuron Models in Terms of Biological Neuron Behaviours</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fikret%20Yalcinkaya">Fikret Yalcinkaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Unsal"> Hamza Unsal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Izhikevich" title="Izhikevich">Izhikevich</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20exponential%20integrate%20fire" title=" adaptive exponential integrate fire"> adaptive exponential integrate fire</a>, <a href="https://publications.waset.org/abstracts/search?q=Hindmarsh%20Rose" title=" Hindmarsh Rose"> Hindmarsh Rose</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20neuron%20behaviours" title=" biological neuron behaviours"> biological neuron behaviours</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking%20neuron%20models" title=" spiking neuron models"> spiking neuron models</a> </p> <a href="https://publications.waset.org/abstracts/91443/comparison-of-spiking-neuron-models-in-terms-of-biological-neuron-behaviours" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2187</span> Brainbow Image Segmentation Using Bayesian Sequential Partitioning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yayun%20Hsu">Yayun Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20Horng-Shing%20Lu"> Henry Horng-Shing Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brainbow" title="brainbow">brainbow</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20imaging" title=" 3D imaging"> 3D imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron%20morphology" title=" neuron morphology"> neuron morphology</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20data%20mining" title=" biological data mining"> biological data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric%20learning" title=" non-parametric learning"> non-parametric learning</a> </p> <a href="https://publications.waset.org/abstracts/2189/brainbow-image-segmentation-using-bayesian-sequential-partitioning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2186</span> The Problems of Current Earth Coordinate System for Earthquake Forecasting Using Single Layer Hierarchical Graph Neuron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benny%20Benyamin%20Nasution">Benny Benyamin Nasution</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Widia%20Sembiring"> Rahmat Widia Sembiring</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahman%20Dalimunthe"> Abdul Rahman Dalimunthe</a>, <a href="https://publications.waset.org/abstracts/search?q=Nursiah%20Mustari"> Nursiah Mustari</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisfan%20Bahri"> Nisfan Bahri</a>, <a href="https://publications.waset.org/abstracts/search?q=Berta%20br%20Ginting"> Berta br Ginting</a>, <a href="https://publications.waset.org/abstracts/search?q=Riadil%20Akhir%20Lubis"> Riadil Akhir Lubis</a>, <a href="https://publications.waset.org/abstracts/search?q=Rita%20Tavip%20Megawati"> Rita Tavip Megawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Indri%20Dithisari"> Indri Dithisari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20graph%20neuron" title="hierarchical graph neuron">hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20hierarchical%20graph%20neuron" title=" multidimensional hierarchical graph neuron"> multidimensional hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20layer%20hierarchical%20graph%20neuron" title=" single layer hierarchical graph neuron"> single layer hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disaster%20forecasting" title=" natural disaster forecasting"> natural disaster forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting" title=" earthquake forecasting"> earthquake forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earth%20coordinate%20system" title=" earth coordinate system"> earth coordinate system</a> </p> <a href="https://publications.waset.org/abstracts/118471/the-problems-of-current-earth-coordinate-system-for-earthquake-forecasting-using-single-layer-hierarchical-graph-neuron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2185</span> Neuron-Based Control Mechanisms for a Robotic Arm and Hand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nishant%20Singh">Nishant Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20Huyck"> Christian Huyck</a>, <a href="https://publications.waset.org/abstracts/search?q=Vaibhav%20Gandhi"> Vaibhav Gandhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Jones"> Alexander Jones</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A robotic arm and hand controlled by simulated neurons is presented. The robot makes use of a biological neuron simulator using a point neural model. The neurons and synapses are organised to create a finite state automaton including neural inputs from sensors, and outputs to effectors. The robot performs a simple pick-and-place task. This work is a proof of concept study for a longer term approach. It is hoped that further work will lead to more effective and flexible robots. As another benefit, it is hoped that further work will also lead to a better understanding of human and other animal neural processing, particularly for physical motion. This is a multidisciplinary approach combining cognitive neuroscience, robotics, and psychology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cell%20assembly" title="cell assembly">cell assembly</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20sensitive%20resistor" title=" force sensitive resistor"> force sensitive resistor</a>, <a href="https://publications.waset.org/abstracts/search?q=robot" title=" robot"> robot</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking%20neuron" title=" spiking neuron"> spiking neuron</a> </p> <a href="https://publications.waset.org/abstracts/65624/neuron-based-control-mechanisms-for-a-robotic-arm-and-hand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65624.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2184</span> Analysis of Sound Loss from the Highway Traffic through Lightweight Insulating Concrete Walls and Artificial Neural Network Modeling of Sound Transmission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Tosun">Mustafa Tosun</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevser%20Dincer"> Kevser Dincer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, analysis on whether the lightweight concrete walled structures used in four climatic regions of Turkey are also capable of insulating sound was conducted. As a new approach, first the wall’s thermal insulation sufficiency’s were calculated and then, artificial neural network (ANN) modeling was used on their cross sections to check if they are sound transmitters too. The ANN was trained and tested by using MATLAB toolbox on a personal computer. ANN input parameters that used were thickness of lightweight concrete wall, frequency and density of lightweight concrete wall, while the transmitted sound was the output parameter. When the results of the TS analysis and those of ANN modeling are evaluated together, it is found from this study, that sound transmit loss increases at higher frequencies, higher wall densities and with larger wall cross sections. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neuron%20network" title="artificial neuron network">artificial neuron network</a>, <a href="https://publications.waset.org/abstracts/search?q=lightweight%20concrete" title=" lightweight concrete"> lightweight concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20insulation" title=" sound insulation"> sound insulation</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20transmit%20loss" title=" sound transmit loss"> sound transmit loss</a> </p> <a href="https://publications.waset.org/abstracts/41076/analysis-of-sound-loss-from-the-highway-traffic-through-lightweight-insulating-concrete-walls-and-artificial-neural-network-modeling-of-sound-transmission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41076.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">252</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2183</span> Creating a Virtual Perception for Upper Limb Rehabilitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nina%20Robson">Nina Robson</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20John%20Faller%20II"> Kenneth John Faller II</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishalkumar%20Ahir"> Vishalkumar Ahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Ricardo%20Deps%20Miguel%20Ferreira"> Arthur Ricardo Deps Miguel Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Buchanan"> John Buchanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Amarnath%20Banerjee"> Amarnath Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the development of a virtual-reality system ARWED, which will be used in physical rehabilitation of patients with reduced upper extremity mobility to increase limb Active Range of Motion (AROM). The ARWED system performs a symmetric reflection and real-time mapping of the patient’s healthy limb on to their most affected limb, tapping into the mirror neuron system and facilitating the initial learning phase. Using the ARWED, future experiments will test the extension of the action-observation priming effect linked to the mirror-neuron system on healthy subjects and then stroke patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physical%20rehabilitation" title="physical rehabilitation">physical rehabilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=mirror%20neuron" title=" mirror neuron"> mirror neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title=" virtual reality"> virtual reality</a>, <a href="https://publications.waset.org/abstracts/search?q=stroke%20therapy" title=" stroke therapy"> stroke therapy</a> </p> <a href="https://publications.waset.org/abstracts/60548/creating-a-virtual-perception-for-upper-limb-rehabilitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60548.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2182</span> Cooperative Coevolution for Neuro-Evolution of Feed Forward Networks for Time Series Prediction Using Hidden Neuron Connections</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravneil%20Nand">Ravneil Nand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cooperative%20coevaluation" title="cooperative coevaluation">cooperative coevaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=feed%20forward%20network" title=" feed forward network"> feed forward network</a>, <a href="https://publications.waset.org/abstracts/search?q=problem%20decomposition" title=" problem decomposition"> problem decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=synapse" title=" synapse"> synapse</a> </p> <a href="https://publications.waset.org/abstracts/29237/cooperative-coevolution-for-neuro-evolution-of-feed-forward-networks-for-time-series-prediction-using-hidden-neuron-connections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29237.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2181</span> A Virtual Electrode through Summation of Time Offset Pulses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Cassar">Isaac Cassar</a>, <a href="https://publications.waset.org/abstracts/search?q=Trevor%20Davis"> Trevor Davis</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi-Kai%20Lo"> Yi-Kai Lo</a>, <a href="https://publications.waset.org/abstracts/search?q=Wentai%20Liu"> Wentai Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retinal prostheses have been successful in eliciting visual responses in implanted subjects. As these prostheses progress, one of their major limitations is the need for increased resolution. As an alternative to increasing the number of electrodes, virtual electrodes may be used to increase the effective resolution of current electrode arrays. This paper presents a virtual electrode technique based upon time-offsets between stimuli. Two adjacent electrodes are stimulated with identical pulses with too short of pulse widths to activate a neuron, but one has a time offset of one pulse width. A virtual electrode of twice the pulse width was then shown to appear in the center, with a total width capable of activating a neuron. This can be used in retinal implants by stimulating electrodes with pulse widths short enough to not elicit responses in neurons, but with their combined pulse width adequate to activate a neuron in between them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20stimulation" title="electrical stimulation">electrical stimulation</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroprosthesis" title=" neuroprosthesis"> neuroprosthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20implant" title=" retinal implant"> retinal implant</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20prosthesis" title=" retinal prosthesis"> retinal prosthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20electrode" title=" virtual electrode"> virtual electrode</a> </p> <a href="https://publications.waset.org/abstracts/14443/a-virtual-electrode-through-summation-of-time-offset-pulses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2180</span> Manual Dexterity in Patients with Motor Neuron Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdalena%20Barbara%20Kaziuk">Magdalena Barbara Kaziuk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilona%20Hubner"> Ilona Hubner</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacek%20Hubner"> Jacek Hubner</a>, <a href="https://publications.waset.org/abstracts/search?q=Slawomir%20Kroczka"> Slawomir Kroczka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The motor neuron disease is a progressive neurodegenerative disease causing malfunction. Irrespective of the form of the disease and its onset always leads to the worsening of the quality of life, with patients usually depending on the family. Materials and methods: The study included 20 persons (5 females, 15 males, aged 65,5 ± 20 years) with clinically certain or probable diagnosis of the motor neuron disease. Patients were examined three times in the period of six months. The diagnosis was established based on the criteria of El Escorial. Manual dexterity was assessed using the test of the card Rene Zazzo and the test of shading in with lines Mira Stambak. Results: All patients achieved unsatisfactory results in Rene Zazzo’s test of the card and most of the patients (60%) in Mira Stambak’s test of shading with lines. Significantly higher test results were achieved for Rene Zazzo’s test and lower test results for Mira Stambak’s test in consecutive measurements. Conclusions: Impairment of manual dexterity is present already at the moment of diagnosing the disease and is growing significantly during its course. The quality of life for MND patients undergoes gradual deterioration as a result of the malfunction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=manual%20dexterity" title="manual dexterity">manual dexterity</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20neuron%20disease" title=" motor neuron disease"> motor neuron disease</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20life" title=" quality of life"> quality of life</a>, <a href="https://publications.waset.org/abstracts/search?q=malfunction" title=" malfunction"> malfunction</a> </p> <a href="https://publications.waset.org/abstracts/88619/manual-dexterity-in-patients-with-motor-neuron-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88619.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2179</span> Probing Neuron Mechanics with a Micropipette Force Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madeleine%20Anthonisen">Madeleine Anthonisen</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hussain%20Sangji"> M. Hussain Sangji</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Monserratt%20Lopez-Ayon"> G. Monserratt Lopez-Ayon</a>, <a href="https://publications.waset.org/abstracts/search?q=Margaret%20Magdesian"> Margaret Magdesian</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Grutter"> Peter Grutter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advances in micromanipulation techniques and real-time particle tracking with nanometer resolution have enabled biological force measurements at scales relevant to neuron mechanics. An approach to precisely control and maneuver neurite-tethered polystyrene beads is presented. Analogous to an Atomic Force Microscope (AFM), this multi-purpose platform is a force sensor with imaging acquisition and manipulation capabilities. A mechanical probe composed of a micropipette with its tip fixed to a functionalized bead is used to incite the formation of a neurite in a sample of rat hippocampal neurons while simultaneously measuring the tension in said neurite as the sample is pulled away from the beaded tip. With optical imaging methods, a force resolution of 12 pN is achieved. Moreover, the advantages of this technique over alternatives such as AFM, namely ease of manipulation which ultimately allows higher throughput investigation of the mechanical properties of neurons, is demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=axonal%20growth" title="axonal growth">axonal growth</a>, <a href="https://publications.waset.org/abstracts/search?q=axonal%20guidance" title=" axonal guidance"> axonal guidance</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20probe" title=" force probe"> force probe</a>, <a href="https://publications.waset.org/abstracts/search?q=pipette%20micromanipulation" title=" pipette micromanipulation"> pipette micromanipulation</a>, <a href="https://publications.waset.org/abstracts/search?q=neurite%20tension" title=" neurite tension"> neurite tension</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron%20mechanics" title=" neuron mechanics"> neuron mechanics</a> </p> <a href="https://publications.waset.org/abstracts/62618/probing-neuron-mechanics-with-a-micropipette-force-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62618.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2178</span> Artificial Intelligence Created Inventions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Goodhue">John Goodhue</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaonan%20Wei"> Xiaonan Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current legal decisions and policies regarding the naming as artificial intelligence as inventor are reviewed with emphasis on the recent decisions by the European Patent Office regarding the DABUS inventions holding that an artificial intelligence machine cannot be an inventor. Next, a set of hypotheticals is introduced and examined to better understand how artificial intelligence might be used to create or assist in creating new inventions and how application of existing or proposed changes in the law would affect the ability to protect these inventions including due to restrictions on artificial intelligence for being named as inventors, ownership of inventions made by artificial intelligence, and the effects on legal standards for inventiveness or obviousness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=invention" title=" invention"> invention</a>, <a href="https://publications.waset.org/abstracts/search?q=patent" title=" patent"> patent</a> </p> <a href="https://publications.waset.org/abstracts/121367/artificial-intelligence-created-inventions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2177</span> DeepNIC a Method to Transform Each Tabular Variable into an Independant Image Analyzable by Basic CNNs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20J.%20M.">Nguyen J. M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucas%20G."> Lucas G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruan%20S."> Ruan S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Digonnet%20H."> Digonnet H.</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonioli%20D."> Antonioli D.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tabular%20data" title="tabular data">tabular data</a>, <a href="https://publications.waset.org/abstracts/search?q=CNNs" title=" CNNs"> CNNs</a>, <a href="https://publications.waset.org/abstracts/search?q=NICs" title=" NICs"> NICs</a>, <a href="https://publications.waset.org/abstracts/search?q=DeepNICs" title=" DeepNICs"> DeepNICs</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20of%20perfect%20trees" title=" random forest of perfect trees"> random forest of perfect trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/166192/deepnic-a-method-to-transform-each-tabular-variable-into-an-independant-image-analyzable-by-basic-cnns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166192.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2176</span> Artificial Neurons Based on Memristors for Spiking Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Yu">Yan Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Yu"> Wang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Xintong"> Chen Xintong</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Yi"> Liu Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Yanzhong"> Zhang Yanzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Yanji"> Wang Yanji</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Xingyu"> Chen Xingyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Miaocheng"> Zhang Miaocheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20Yi"> Tong Yi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to its high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO₂-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO₂-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=leaky%20integrate-and-fire" title="leaky integrate-and-fire">leaky integrate-and-fire</a>, <a href="https://publications.waset.org/abstracts/search?q=memristor" title=" memristor"> memristor</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking%20neural%20networks" title=" spiking neural networks"> spiking neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking-time-dependent-plasticity" title=" spiking-time-dependent-plasticity"> spiking-time-dependent-plasticity</a> </p> <a href="https://publications.waset.org/abstracts/147746/artificial-neurons-based-on-memristors-for-spiking-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147746.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2175</span> Analysis of iPSC-Derived Dopaminergic Neuron Susceptibility to Influenza and Excitotoxicity in Non-Affective Psychosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamileh%20Ahmed">Jamileh Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Helena%20Hernandez"> Helena Hernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20De%20Erausquin"> Gabriel De Erausquin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> H1N1 virus susceptibility of iPSC-derived DA neurons from schizophrenia patients and controls will compared. C57/BL-6 fibroblasts were reprogrammed into iPSCs using a lenti-viral vector containing SOKM genes. Pluripotency verification with the AP assay and immunocytochemistry ensured iPSC presence. The experimental outcome of ISPCs from DA neuron differentiation will be discussed in the Results section. Fibroblasts from patients and controls will be reprogrammed into iPSCs using a sendai-virus vector containing SOKM. IPSCs will be characterized using the AP assay, immunocytochemistry and RT-PCR. IPSCs will then be differentiated into DA neurons. Gene methylation will be compared for both groups with custom-designed microarrays. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=schizophrenia" title="schizophrenia">schizophrenia</a>, <a href="https://publications.waset.org/abstracts/search?q=iPSCs" title=" iPSCs"> iPSCs</a>, <a href="https://publications.waset.org/abstracts/search?q=stem%20cells" title=" stem cells"> stem cells</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroscience" title=" neuroscience"> neuroscience</a> </p> <a href="https://publications.waset.org/abstracts/27801/analysis-of-ipsc-derived-dopaminergic-neuron-susceptibility-to-influenza-and-excitotoxicity-in-non-affective-psychosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27801.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">429</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2174</span> Action Potential of Lateral Geniculate Neurons at Low Threshold Currents: Simulation Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faris%20Tarlochan">Faris Tarlochan</a>, <a href="https://publications.waset.org/abstracts/search?q=Siva%20Mahesh%20Tangutooru"> Siva Mahesh Tangutooru </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lateral Geniculate Nucleus (LGN) is the relay center in the visual pathway as it receives most of the input information from retinal ganglion cells (RGC) and sends to visual cortex. Low threshold calcium currents (IT) at the membrane are the unique indicator to characterize this firing functionality of the LGN neurons gained by the RGC input. According to the LGN functional requirements such as functional mapping of RGC to LGN, the morphologies of the LGN neurons were developed. During the neurological disorders like glaucoma, the mapping between RGC and LGN is disconnected and hence stimulating LGN electrically using deep brain electrodes can restore the functionalities of LGN. A computational model was developed for simulating the LGN neurons with three predominant morphologies, each representing different functional mapping of RGC to LGN. The firings of action potentials at LGN neuron due to IT were characterized by varying the stimulation parameters, morphological parameters and orientation. A wide range of stimulation parameters (stimulus amplitude, duration and frequency) represents the various strengths of the electrical stimulation with different morphological parameters (soma size, dendrites size and structure). The orientation (0-1800) of LGN neuron with respect to the stimulating electrode represents the angle at which the extracellular deep brain stimulation towards LGN neuron is performed. A reduced dendrite structure was used in the model using Bush–Sejnowski algorithm to decrease the computational time while conserving its input resistance and total surface area. The major finding is that an input potential of 0.4 V is required to produce the action potential in the LGN neuron which is placed at 100 µm distance from the electrode. From this study, it can be concluded that the neuroprostheses under design would need to consider the capability of inducing at least 0.4V to produce action potentials in LGN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lateral%20Geniculate%20Nucleus" title="Lateral Geniculate Nucleus">Lateral Geniculate Nucleus</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20cortex" title=" visual cortex"> visual cortex</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element" title=" finite element"> finite element</a>, <a href="https://publications.waset.org/abstracts/search?q=glaucoma" title=" glaucoma"> glaucoma</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroprostheses" title=" neuroprostheses"> neuroprostheses</a> </p> <a href="https://publications.waset.org/abstracts/38655/action-potential-of-lateral-geniculate-neurons-at-low-threshold-currents-simulation-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38655.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2173</span> Regression of Hand Kinematics from Surface Electromyography Data Using an Long Short-Term Memory-Transformer Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Sadat%20Sadati%20Rostami">Anita Sadat Sadati Rostami</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Almasi%20Ghaleh"> Reza Almasi Ghaleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surface electromyography (sEMG) offers important insights into muscle activation and has applications in fields including rehabilitation and human-computer interaction. The purpose of this work is to predict the degree of activation of two joints in the index finger using an LSTM-Transformer architecture trained on sEMG data from the Ninapro DB8 dataset. We apply advanced preprocessing techniques, such as multi-band filtering and customizable rectification methods, to enhance the encoding of sEMG data into features that are beneficial for regression tasks. The processed data is converted into spike patterns and simulated using Leaky Integrate-and-Fire (LIF) neuron models, allowing for neuromorphic-inspired processing. Our findings demonstrate that adjusting filtering parameters and neuron dynamics and employing the LSTM-Transformer model improves joint angle prediction performance. This study contributes to the ongoing development of deep learning frameworks for sEMG analysis, which could lead to improvements in motor control systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20electromyography" title="surface electromyography">surface electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM-transformer" title=" LSTM-transformer"> LSTM-transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=spiking%20neural%20networks" title=" spiking neural networks"> spiking neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20kinematics" title=" hand kinematics"> hand kinematics</a>, <a href="https://publications.waset.org/abstracts/search?q=leaky%20integrate-and-fire%20neuron" title=" leaky integrate-and-fire neuron"> leaky integrate-and-fire neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=band-pass%20filtering" title=" band-pass filtering"> band-pass filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=muscle%20activity%20decoding" title=" muscle activity decoding"> muscle activity decoding</a> </p> <a href="https://publications.waset.org/abstracts/194648/regression-of-hand-kinematics-from-surface-electromyography-data-using-an-long-short-term-memory-transformer-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194648.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">7</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2172</span> Massively-Parallel Bit-Serial Neural Networks for Fast Epilepsy Diagnosis: A Feasibility Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Si%20Mon%20Kueh">Si Mon Kueh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tom%20J.%20Kazmierski"> Tom J. Kazmierski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Networks%20%28ANN%29" title="Artificial Neural Networks (ANN)">Artificial Neural Networks (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=bit-serial%20neural%20processor" title=" bit-serial neural processor"> bit-serial neural processor</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=Neural%20Processing%20Element%20%28NPE%29" title=" Neural Processing Element (NPE)"> Neural Processing Element (NPE)</a> </p> <a href="https://publications.waset.org/abstracts/41406/massively-parallel-bit-serial-neural-networks-for-fast-epilepsy-diagnosis-a-feasibility-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41406.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2171</span> Modeling of Bioelectric Activity of Nerve Cells Using Bond Graph Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ghasemi">M. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Eskandari"> F. Eskandari</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hamzehei"> B. Hamzehei</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Arshi"> A. R. Arshi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioelectric activity of nervous cells might be changed causing by various factors. This alteration can lead to unforeseen circumstances in other organs of the body. Therefore, the purpose of this study was to model a single neuron and its behavior under an initial stimulation. This study was developed based on cable theory by means of the Bond Graph method. The numerical values of the parameters were derived from empirical studies of cellular electrophysiology experiments. Initial excitation was applied through square current functions, and the resulted action potential was estimated along the neuron. The results revealed that the model was developed in this research adapted with the results of experimental studies and demonstrated the electrical behavior of nervous cells properly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bond%20graph" title="bond graph">bond graph</a>, <a href="https://publications.waset.org/abstracts/search?q=stimulation" title=" stimulation"> stimulation</a>, <a href="https://publications.waset.org/abstracts/search?q=nervous%20cells" title=" nervous cells"> nervous cells</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a> </p> <a href="https://publications.waset.org/abstracts/32701/modeling-of-bioelectric-activity-of-nerve-cells-using-bond-graph-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32701.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">429</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2170</span> Artificial Intelligence and Personhood: An African Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meshandren%20Naidoo">Meshandren Naidoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Amy%20Gooden"> Amy Gooden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The concept of personhood extending from the moral status of an artificial intelligence system has been explored – but predominantly from a Western conception of personhood. African personhood, however, is distinctly different from Western personhood in that communitarianism is central rather than individualism. Given the decolonization projects happening in Africa, it’s paramount to consider these views. This research demonstrates that the African notion of personhood may extend for an artificial intelligent system where the pre-conditions are met. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=ethics" title=" ethics"> ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=law" title=" law"> law</a>, <a href="https://publications.waset.org/abstracts/search?q=personhood" title=" personhood"> personhood</a>, <a href="https://publications.waset.org/abstracts/search?q=policy" title=" policy"> policy</a> </p> <a href="https://publications.waset.org/abstracts/153439/artificial-intelligence-and-personhood-an-african-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2169</span> Artificial Intelligence and Police</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrnoosh%20Abouzari">Mehrnoosh Abouzari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial intelligence has covered all areas of human life and has helped or replaced many jobs. One of the areas of application of artificial intelligence in the police is to detect crime, identify the accused or victim and prove the crime. It will play an effective role in implementing preventive justice and creating security in the community, and improving judicial decisions. This will help improve the performance of the police, increase the accuracy of criminal investigations, and play an effective role in preventing crime and high-risk behaviors in society. This article presents and analyzes the capabilities and capacities of artificial intelligence in police and similar examples used worldwide to prove the necessity of using artificial intelligence in the police. The main topics discussed include the performance of artificial intelligence in crime detection and prediction, the risk capacity of criminals and the ability to apply arbitray institutions, and the introduction of artificial intelligence programs implemented worldwide in the field of criminal investigation for police. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=police" title="police">police</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=prevention" title=" prevention"> prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=software" title=" software"> software</a> </p> <a href="https://publications.waset.org/abstracts/141793/artificial-intelligence-and-police" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141793.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2168</span> The Intersection/Union Region Computation for Drosophila Brain Images Using Encoding Schemes Based on Multi-Core CPUs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming-Yang%20Guo">Ming-Yang Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Xian%20Wu"> Cheng-Xian Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Xiang%20Chen"> Wei-Xiang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Yuan%20Lin"> Chun-Yuan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yen-Jen%20Lin"> Yen-Jen Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ann-Shyn%20Chiang"> Ann-Shyn Chiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Drosophila%20driver%20image" title="Drosophila driver image">Drosophila driver image</a>, <a href="https://publications.waset.org/abstracts/search?q=Drosophila%20neuron%20images" title=" Drosophila neuron images"> Drosophila neuron images</a>, <a href="https://publications.waset.org/abstracts/search?q=intersection%2Funion%20computation" title=" intersection/union computation"> intersection/union computation</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20processing" title=" parallel processing"> parallel processing</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenMP" title=" OpenMP"> OpenMP</a> </p> <a href="https://publications.waset.org/abstracts/89335/the-intersectionunion-region-computation-for-drosophila-brain-images-using-encoding-schemes-based-on-multi-core-cpus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89335.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2167</span> The Role of Artificial Intelligence in Concrete Constructions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ardalan%20Tofighi%20Soleimandarabi">Ardalan Tofighi Soleimandarabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial intelligence has revolutionized the concrete construction industry and improved processes by increasing efficiency, accuracy, and sustainability. This article examines the applications of artificial intelligence in predicting the compressive strength of concrete, optimizing mixing plans, and improving structural health monitoring systems. Artificial intelligence-based models, such as artificial neural networks (ANN) and combined machine learning techniques, have shown better performance than traditional methods in predicting concrete properties. In addition, artificial intelligence systems have made it possible to improve quality control and real-time monitoring of structures, which helps in preventive maintenance and increases the life of infrastructure. Also, the use of artificial intelligence plays an effective role in sustainable construction by optimizing material consumption and reducing waste. Although the implementation of artificial intelligence is associated with challenges such as high initial costs and the need for specialized training, it will create a smarter, more sustainable, and more affordable future for concrete structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20construction" title=" concrete construction"> concrete construction</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20strength%20prediction" title=" compressive strength prediction"> compressive strength prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a> </p> <a href="https://publications.waset.org/abstracts/192069/the-role-of-artificial-intelligence-in-concrete-constructions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">15</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2166</span> Activation of Mirror Neuron System Response to Drumming Training: A Functional Magnetic Resonance Imaging Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manal%20Alosaimi">Manal Alosaimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many rehabilitation strategies exist to aid persons with neurological disorders relearn motor skills through intensive training. Evidence supporting the theory that cortical areas involved in motor execution can be triggered by observing actions performed by others is attributed to the function of the mirror neuron system (MNS) indicates that activation of the MNS is associated with improvements in physical action and motor learning. Therefore, it is important to investigate the relationship between motor training (in this case, playing the drums) and the activation of the MNS. To achieve this, 15 healthy right-handed participants received drum-kit training for 21 weeks, during which time blood oxygen level-dependent (BOLD) signals were monitored in the brain using functional magnetic resonance imaging (fMRI). Participants were required to perform action–observation and action–execution fMRI tasks. The main results are that BOLD signals in classical regions of the MNS such as supramarginal gyri, inferior parietal lobule, and supplementary motor area increase significantly over the training period. Activation of these areas indicates that passive-observation of others performing these same skills may facilitate recovery of persons suffering from neurological disorders, and complement conventional rehabilitation programs that focus on action execution or intense training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fMRI" title="fMRI">fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=mirror%20neuron%20system" title=" mirror neuron system"> mirror neuron system</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroplasticity" title=" neuroplasticity"> neuroplasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=drumming" title=" drumming"> drumming</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=music" title=" music"> music</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20observation" title=" action observation"> action observation</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20execution" title=" action execution"> action execution</a> </p> <a href="https://publications.waset.org/abstracts/186635/activation-of-mirror-neuron-system-response-to-drumming-training-a-functional-magnetic-resonance-imaging-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=73">73</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=74">74</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20neuron&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>