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The emulation of synapses is essential to neuromorphic computing systems. Despite remarkable progress has been made in the two-terminal device (memristor), three-terminal transistors evoke greater attention because of the controlled conductance between the source and drain. The purpose of this paper is to investigate the synaptic plasticity of the TiO₂ nanowire transistor.

TiO₂ nanowire transistor was assembled by dielectrophoresis, and the synaptic plasticity such as paired-pulse facilitation, learning behaviors and high-pass filter were studied.

Facilitation index decreases with the increasing pulse interval. A bigger response current is obtained at the pulses with higher amplitude and smaller intervals, which is similar to the consolidated memory at the deeply and frequently learning. The increased current at the higher stimulus frequency demonstrates a promising application in the high-pass filter.

TiO₂ nanowire transistors possess broad application prospects in the future neural network.

Drones are used in several purposes including examining areas, mapping surroundings and rescue mission operations. During these tasks, they could encounter compound surroundings having multiple obstacles, acute edges and deadlocks. The purpose of this paper is to propose an obstacle dodging technique required to move the drones autonomously and generate the obstacle's map of an unknown place dynamically.

Therefore, an obstacle dodging technique is essentially required to move autonomously. The automaton of drones requires complicated vision sensors and a high computing force. During this research, a methodology that uses two basic ultrasonic-oriented proximity sensors placed at the center of the drone and applies neural control using synaptic plasticity for dynamic obstacle avoidance is proposed. The two-neuron intermittent system has been established by neural control. The synaptic plasticity is used to find turning angles from different viewpoints with immediate remembrance, so it helps in decision-making for a drone. Hence, the automaton will be able to travel around and modify its angle of turning for escaping objects during the route in unknown surroundings with narrow junctions and dead ends. Furthermore, wherever an obstacle is detected during the route, the coordinate information is communicated using RESTful Web service to an android app and an obstacle map is generated according to the information sent by the drone. In this research, the drone is successfully designed and automated and an obstacle map using the V-REP simulation environment is generated.

Simulation results show that the drone effectively moves and turns around the obstacles and the experiment of using web services with the drone is also successful in generating the obstacle's map dynamically.

The obstacle map generated by autonomous drone is useful in many applications such as examining fields, mapping surroundings and rescue mission operations.

This paper aims to link recent findings in cognitive neuroscience to better understand how andragogically informed instructional practices impact cognition and learning.

The research questions guiding the study is in what ways can the recent findings in cognitive neuroscience help to inform adult education theory, including andragogy in particular, to deepen our understanding of how andragogical instructional principles and practices can improve learning? We adopted Torraco’s (2005) integrative literature review approach of providing enough details regarding the selection of the literature and the identification and verification of emerged themes of main ideas.

The core assumptions of andragogy (self-direction, prior experience, readiness to learn and immediacy of application) have a connection to the neural networks related to memory and cognition.

First, this study provides fundamental foundations for combining cognitive neuroscience and adult learning to illuminate how cognitive neuroscience contributes physiologically to adult learning. Second, the findings in cognitive neuroscience related to the four assumptions for andragogy help to provide scientific explanations and interpretations for adult learning theories influencing human resource development (HRD), such as self-directed learning, experiential learning and role theory.

First, HRD practitioners could use the integrative approach between andragogy and the cognitive neuroscience to reduce the issues of learning activities in generation differences. In addition, cognitive neuroscience research may contribute to improving teaching and instructional techniques.

The contributions of this study is that it provides an integrative review about why and how anagogical principles work through the lens of cognitive neuroscience. Based on the findings, we suggested a model of adaptive cognitive neuroscience-adult learning structures.

The purpose of this paper is to propose an integrative framework bringing together results from neuroplasticity and decision-making from a neuroscience perspective with those from market plasticity, i.e. with which practices market actors shape markets.

Provided that developments in neuroscience indicate that training the brain for orientation toward efficient decision-making processes under uncertainty is possible, an in-depth analysis can be conducted by using the integrative framework, which was set up by the authors for advancing research efforts in neuroeconomics and neurofinance on these lines.

Markets have a plastic character; they can change shape and form and remain in that way thereafter. The marketers have always been causing this change to succeed in their marketing strategies and efforts. Plasticity, hitherto considered by marketing, market sociology and evolutionary economics, has a potential in financial decision-making processes, especially regarding its role in training the brain for stable financial decisions.

The theoretical approach can be incorporated for delivering an alternative representation of the knowledge processes associated with financial decisions.

The practical approach can be used for improving the practical aspects of financial decision-making processes.

The contribution is the first of its kind which integrates neuroscience approaches of plasticity and decision-making with the concept of market plasticity from the literature on economics and management, showing their similarities and opening a new front of discussion on how these two approaches can learn from each other to increase the explanatory power of financial decision-making processes and to gain new insights for financial decision makers on how to make more efficient financial decisions in the times of uncertainty.

How does the human brain absorb information and turn it into skills of its own in psychotherapy? In an attempt to answer this question, the authors will review the intricacies of processing channels in psychotherapy and propose the term transprocessing (as in transduction and processing combined) for the underlying mechanisms. Through transprocessing the brain processes multimodal memories and creates reparative solutions in the course of psychotherapy. Transprocessing is proposed as a stage-sequenced mechanism of deconstruction of engrained patterns of response. Through psychotherapy, emotional-cognitive reintegration and its consolidation is accomplished. This process is mediated by cellular and neural plasticity changes.

The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.

This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.

The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.

This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.

Noninvasive brain stimulation (NIBS) such a transcranial magnetic stimulation, intermittent theta burst stimulation, transcranial direct current stimulation and electroconvulsive therapy have emerged as an efficacious and well-tolerated therapy for treatment-resistant psychiatric disorders. While novel NIBS techniques are an exciting addition to the current repertoire of neuropsychiatric therapies, their success is somewhat limited by the wide range of treatment responses seen among treated patients.

In this study, the authors will review the studies on relevant genetic polymorphisms and discuss the role of RNA genotyping in personalizing NIBS.

Genome studies have revealed several genetic polymorphisms that may contribute for the heterogeneity of treatment response to NIBS where the presence of certain single nucleotide polymorphisms (SNPs) are associated with responders versus nonresponders.

Historically, mental illnesses have been arguably some of the most challenging disorders to study and to treat because of the degree of biological variability across affected individuals, the role of genetic and epigenetic modifications, the diversity of clinical symptomatology and presentations and the interplay with environmental factors. In lieu of these challenges, there has been a push for personalized medicine in psychiatry that aims to optimize treatment response based on one’s unique characteristics.

The purpose of this paper is to provide description and an analysis of two worlds colliding where real‐world roles or ideas play out in a virtual dimension. Inhabited by digital natives, the virtual world in a learning organization is a journey back to the future of microworlds where the only limitation is one's imagination.

In 1990, when computer technology flexs its range of useful possibilities, Senge envisions its practical application in a learning organization. He purports the use of computer simulations which he calls microworlds, as a virtual sandbox for learning. His vision, is expanded today, means virtual worlds that co‐exist with the real world. Second life is an example of that virtual world. The possibilities for learning inherent in this virtual world seem limitless in a knowledge‐driven, global society hungry for the next creative and innovative way of transforming the world, real or virtual.

Digital natives are the drivers of change who will explore the brave new world of computer simulations. As simulations become more technologically infuse with artificial intelligence, its application for education and learning will broaden and expand. Unlike the static interface of a textbook, which shows limitations in content scope and delivery, this virtual world knows no limits in knowledge expansion. Validated course content, formal and informal knowledge contributions from peers and experts alike, networked knowledge coming from Web resources and the internet enrich the learner's ability beyond measure to experience the world and know it better and more intimately.

The emergent technology of virtual worlds utilizing simulations of real life work situations is a throwback to the microworlds of yesterday. They are ideal for conducting thought experiments that deepen with experiential understanding. This technology‐mediated form of learning affords the opportunity to experience the results of an action which may take a lifetime to learn in real time. This virtual world allows decision‐making but eliminates the risks of serious, unintended consequences. It is a wonderful resource for living vicariously experiences which are unavailable or unlikely in the real world.

Virtual simulations are useful for learning concepts, ideas, and assumptions that are difficult to perform or test in the physical dimension. Digital natives, people who were born in the 1980s, explore Second Life, and the paper discusses the value of this virtual world.

Tuberous sclerosis complex (TSC) is a genetic disorder with a high prevalence of autism spectrum disorder (ASD), yet no single genetic, neurological or neurophysiological risk marker is necessary or sufficient to increase risk for ASD. This paper aims to discuss the utility of adopting a developmental perspective.

The increasing number of TSC infants presenting with abnormalities prenatally provides a unique opportunity to study risk pathways to ASD from birth. Here, the authors review findings to date that support the investigation of infants with TSC to further our understanding of typical and atypical development.

Evidence has accumulated from studies of infants at familial risk for ASD (“baby siblings”) to suggest that early markers of ASD are present in the first year of life. The early waves of prospective studies of infants with TSC indicate dynamic changes in developmental trajectories to ASD and are likely to provide insight into cascading effects of brain “insult” early in development. Emerging evidence of phenotypic and biological homology between syndromic and idiopathic cases of ASD supports the notion of a convergence of risk factors on a final common pathway in ASD.

The delineation of brain-based biomarkers of risk, prediction and treatment response in TSC will be critical in aiding the development of targeted intervention and prevention strategies for those infants at high risk of poorer developmental outcomes.