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1
Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "Brain-Inspired Agents for Quantum Reinforcement Learning." Mathematics 12, no. 8 (April 19, 2024): 1230. http://dx.doi.org/10.3390/math12081230.
Анотація:
In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quantum spiking neural networks (QSNN) and quantum long short-term memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conducted a comparative analysis of our model against existing quantum models, including quantum neural networks (QNNs), QLSTM, QSNN and their classical counterparts, elucidating its unique contributions. Through empirical results, we demonstrated the effectiveness of utilizing quantum models inspired by the brain, which outperform the classical approaches and other quantum models in optimizing energy use case. Specifically, in terms of average, best and worst total reward, test reward, robustness, and learning curve.
2
Ma, Gehua, He Wang, Jingyuan Zhao, Rui Yan, and Huajin Tang. "Successive POI Recommendation via Brain-Inspired Spatiotemporal Aware Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 574–82. http://dx.doi.org/10.1609/aaai.v38i1.27813.
Анотація:
Existing approaches usually perform spatiotemporal representation in the spatial and temporal dimensions, respectively, which isolates the spatial and temporal natures of the target and leads to sub-optimal embeddings. Neuroscience research has shown that the mammalian brain entorhinal-hippocampal system provides efficient graph representations for general knowledge. Moreover, entorhinal grid cells present concise spatial representations, while hippocampal place cells represent perception conjunctions effectively. Thus, the entorhinal-hippocampal system provides a novel angle for spatiotemporal representation, which inspires us to propose the SpatioTemporal aware Embedding framework (STE) and apply it to POIs (STEP). STEP considers two types of POI-specific representations: sequential representation and spatiotemporal conjunctive representation, learned using sparse unlabeled data based on the proposed graph-building policies. Notably, STEP jointly represents the spatiotemporal natures of POIs using both observations and contextual information from integrated spatiotemporal dimensions by constructing a spatiotemporal context graph. Furthermore, we introduce a successive POI recommendation method using STEP, which achieves state-of-the-art performance on two benchmarks. In addition, we demonstrate the excellent performance of the STE representation approach in other spatiotemporal representation-centered tasks through a case study of the traffic flow prediction problem. Therefore, this work provides a novel solution to spatiotemporal representation and paves a new way for spatiotemporal modeling-related tasks.
3
Pham, Trung Quang, Teppei Matsui, and Junichi Chikazoe. "Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review." Biology 12, no. 10 (October 12, 2023): 1330. http://dx.doi.org/10.3390/biology12101330.
Анотація:
Artificial neural networks (ANNs) that are heavily inspired by the human brain now achieve human-level performance across multiple task domains. ANNs have thus drawn attention in neuroscience, raising the possibility of providing a framework for understanding the information encoded in the human brain. However, the correspondence between ANNs and the brain cannot be measured directly. They differ in outputs and substrates, neurons vastly outnumber their ANN analogs (i.e., nodes), and the key algorithm responsible for most of modern ANN training (i.e., backpropagation) is likely absent from the brain. Neuroscientists have thus taken a variety of approaches to examine the similarity between the brain and ANNs at multiple levels of their information hierarchy. This review provides an overview of the currently available approaches and their limitations for evaluating brain–ANN correspondence.
4
S Neves, Fabio, and Marc Timme. "Bio-inspired computing by nonlinear network dynamics—a brief introduction." Journal of Physics: Complexity 2, no. 4 (December 1, 2021): 045019. http://dx.doi.org/10.1088/2632-072x/ac3ad4.
Анотація:
Abstract The field of bio-inspired computing has established a new Frontier for conceptualizing information processing, aggregating knowledge from disciplines as different as neuroscience, physics, computer science and dynamical systems theory. The study of the animal brain has shown that no single neuron or neural circuit motif is responsible for intelligence or other higher-order capabilities. Instead, complex functions are created through a broad variety of circuits, each exhibiting an equally varied repertoire of emergent dynamics. How collective dynamics may contribute to computations still is not fully understood to date, even on the most elementary level. Here we provide a concise introduction to bio-inspired computing via nonlinear dynamical systems. We first provide a coarse overview of how the study of biological systems has catalyzed the development of artificial systems in several broad directions. Second, we discuss how understanding the collective dynamics of spiking neural circuits and model classes thereof, may contribute to and inspire new forms of ‘bio-inspired’ computational paradigms. Finally, as a specific set of examples, we analyze in more detail bio-inspired approaches to computing discrete decisions based on multi-dimensional analogue input signals, via k-winners-take-all functions. This article may thus serve as a brief introduction to the qualitative variety and richness of dynamical bio-inspired computing models, starting broadly and focusing on a general example of computation from current research. We believe that understanding basic aspects of the variety of bio-inspired approaches to computation on the coarse level of first principles (instead of details about specific simulation models) and how they relate to each other, may provide an important step toward catalyzing novel approaches to autonomous and computing machines in general.
5
Kułacz, Łukasz, and Adrian Kliks. "Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks." Journal of Telecommunications and Information Technology 1 (March 29, 2019): 39–46. http://dx.doi.org/10.26636/jtit.2019.130618.
Анотація:
This paper presents developments in the area of brain-inspired wireless communications relied upon in dense wireless networks. Classic approaches to network design are complemented, firstly, by the neuroplasticity feature enabling to add the learning ability to the network. Secondly, the microglia ability enabling to repair a network with damaged neurons is considered. When combined, these two functionalities guarantee a certain level of fault-tolerance and self-repair of the network. This work is inspired primarily by observations of extremely energy efficient functions of the brain, and of the role that microglia cells play in the active immune defense system. The concept is verified by computer simulations, where messages are transferred through a dense wireless network based on the assumption of minimized energy consumption. Simulation encompasses three different network topologies which show the impact that the location of microglia nodes and their quantity exerts on network performance. Based on the results achieved, some algorithm improvements and potential future work directions have been identified.
6
Zheng, Tianyi, Wuhao Yang, Jie Sun, Zhenxi Liu, Kunfeng Wang, and Xudong Zou. "Processing IMU action recognition based on brain-inspired computing with microfabricated MEMS resonators." Neuromorphic Computing and Engineering 2, no. 2 (April 8, 2022): 024004. http://dx.doi.org/10.1088/2634-4386/ac5ddf.
Анотація:
Abstract Reservoir computing (RC) decomposes the recurrent neural network into a fixed network with recursive connections and a trainable linear network. With the advantages of low training cost and easy hardware implementation, it provides a method for the effective processing of time-domain correlation information. In this paper, we build a hardware RC system with a nonlinear MEMS resonator and build an action recognition data set with time-domain correlation. Moreover, two different universal data set are utilized to verify the classification and prediction performance of the RC hardware system. At the same time, the feasibility of the novel data set was validated by three general machine learning approaches. Specifically, the processing of this novel time-domain correlation data set obtained a relatively high success rate. These results, together with the dataset that we build, enable the broad implementation of brain-inspired computing with microfabricated devices, and shed light on the potential for the realization of integrated perception and calculation in our future work.
7
Misra, Durgamadhab. "Special Issue of Interface on Neuromorphic Computing: An Introduction and State of the Field." Electrochemical Society Interface 32, no. 1 (March 1, 2023): 45–46. http://dx.doi.org/10.1149/2.f08231if.
Анотація:
The human brain integrates and processes information to perform complex cognitive tasks within approximately 20 watts of power. Today’s fastest supercomputer is unable to deliver the power requirements and the number of operations at the same energy levels. In the brain, the discrete and sparse events in time called spikes are used to process and encode the information. The energy efficiency of the brain is attributed to the sparsity of the spikes and event-driven communication between the neurons. Complex interconnections among the 1011 neurons and 1015 synapses in the human brain process the information, possibly encoded in the time, frequency, and phase of the spikes. Therefore, to emulate human cognition requires novel electronic devices and new algorithmic approaches. Brain-inspired computing, or neuromorphic computing, is an approach to build energy-efficient computing architectures and systems.
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Flor, Herta, Koichi Noguchi, Rolf-Detlef Treede, and Dennis C. Turk. "The role of evolving concepts and new technologies and approaches in advancing pain research, management, and education since the establishment of the International Association for the Study of Pain." Pain 164, no. 11S (November 2023): S16—S21. http://dx.doi.org/10.1097/j.pain.0000000000003063.
Анотація:
Abstract The decades since the inauguration of the International Association for the Study of Pain have witnessed major advances in scientific concepts (such as the biopsychosocial model and chronic primary pain as a disease in its own right) and in new technologies and approaches (from molecular biology to brain imaging) that have inspired innovations in pain research. These have guided progress in pain management and education about pain for healthcare professionals, the general public, and administrative agencies.
9
Kulhare, Rachna, and S. Veenadhari. "Feature Reduction in Classification Tasks using Bio-inspired Optimization Algorithms." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 04 (December 31, 2022): 72–78. http://dx.doi.org/10.18090/samriddhi.v14i04.12.
Анотація:
In big data, there is a major difficulty that requires data mining to be conducted with elevated data in big technology, which would be gaining a lot of traction nowadays. When it comes to Big Data, feature selection approaches are seen to be a game changer since they can assist minimize the complexity of data, making it simpler to study and translate it into meaningful information. To enhance classification performance, feature selection removes unnecessary and redundant characteristics from the dataset. In this paper, Grey Wolf Approaches based on Quantum leaping neighbor memeplexes termed as QLGWONM is proposed. The result shows that when compared to the some bio-inspired algorithms such as PSO, GWO, ABA, CSA models, the suggested model performed well in terms of accuracy and have accuracy of 100% for Brain Tumor, CNS, Lung dataset and 97.1% for Ionosphere dataset and 99% for NSL-KDD.
10
VASSILIADIS, VASSILIOS, and GEORGIOS DOUNIAS. "NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS." International Journal on Artificial Intelligence Tools 18, no. 04 (August 2009): 487–516. http://dx.doi.org/10.1142/s021821300900024x.
Анотація:
The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.
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Patrikelis, Panayiotis, Lambros Messinis, Vasileios Kimiskidis, and Stylianos Gatzonis. "Neuropsychology of epilepsy surgery and theory-based practice: an opinion review." Arquivos de Neuro-Psiquiatria 81, no. 09 (September 2023): 835–43. http://dx.doi.org/10.1055/s-0043-1770349.
Анотація:
AbstractThe present review attempts to discuss how some of the central concepts from the Lurian corpus of theories are relevant to the modern neuropsychology of epilepsy and epilepsy surgery. Through the lenses of the main Lurian concepts (such as the qualitative syndrome analysis), we discuss the barriers to clinical reasoning imposed by quadrant-based views of the brain, or even atheoretical, statistically-based and data-driven approaches. We further advice towards a systemic view inspired by Luria's clinical work and theorizing, given their importance towards our clinical practice, by contrasting it to the modular views when appropriate. Luria provided theory-guided methods of assessment and rehabilitation of higher cortical functions. Although his work did not specifically address epilepsy, his theory and clinical approaches actually apply to the whole neuropathology spectrum and accounting for the whole panorama of neurocognition. This holistic and systemic approach to the brain is consistent with the network approach of the neuroimaging era. As to epilepsy, the logic of cognitive functions organized into complex functional systems, contrary to modular views of the brain, heralds current knowledge of epilepsy as a network disease, as well as the concept of the functional deficit zone.
12
Darwish, Saad M., Lina J. Abu Shaheen, and Adel A. Elzoghabi. "A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique." Bioengineering 10, no. 7 (July 9, 2023): 819. http://dx.doi.org/10.3390/bioengineering10070819.
Анотація:
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.
13
Rayaprolu, Sruti, Lenora Higginbotham, Pritha Bagchi, Caroline M. Watson, Tian Zhang, Allan I. Levey, Srikant Rangaraju, and Nicholas T. Seyfried. "Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau." Neuropsychopharmacology 46, no. 1 (September 8, 2020): 98–115. http://dx.doi.org/10.1038/s41386-020-00840-3.
Анотація:
AbstractThe repeated failures of amyloid-targeting therapies have challenged our narrow understanding of Alzheimer’s disease (AD) pathogenesis and inspired wide-ranging investigations into the underlying mechanisms of disease. Increasing evidence indicates that AD develops from an intricate web of biochemical and cellular processes that extend far beyond amyloid and tau accumulation. This growing recognition surrounding the diversity of AD pathophysiology underscores the need for holistic systems-based approaches to explore AD pathogenesis. Here we describe how network-based proteomics has emerged as a powerful tool and how its application to the AD brain has provided an informative framework for the complex protein pathophysiology underlying the disease. Furthermore, we outline how the AD brain network proteome can be leveraged to advance additional scientific and translational efforts, including the discovery of novel protein biomarkers of disease.
14
TAN, TSE GUAN, JASON TEO, and PATRICIA ANTHONY. "NATURE-INSPIRED COGNITIVE EVOLUTION TO PLAY MS. PAC-MAN." International Journal of Modern Physics: Conference Series 09 (January 2012): 456–63. http://dx.doi.org/10.1142/s2010194512005545.
Анотація:
Recent developments in nature-inspired computation have heightened the need for research into the three main areas of scientific, engineering and industrial applications. Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. So far however, there has been little discussion about the effectiveness of the application of these models to computer and video games in particular. The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. This paper investigates the abilities of Evolution Strategies (ES) to evolve feed-forward artificial neural network's internal parameters (i.e. weight and bias values) for automatically generating Ms. Pac-man controllers. The main objective of this game is to clear a maze of dots while avoiding the ghosts and to achieve the highest possible score. The experimental results show that an ES-based system can be successfully applied to automatically generate artificial intelligence for a complex, dynamic and highly stochastic video game environment.
15
Irastorza-Valera, Luis, José María Benítez, Francisco J. Montáns, and Luis Saucedo-Mora. "An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates." Biomimetics 9, no. 2 (February 9, 2024): 101. http://dx.doi.org/10.3390/biomimetics9020101.
Анотація:
The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain’s logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.
16
Zacarias-Morales, Noel, Pablo Pancardo, José Adán Hernández-Nolasco, and Matias Garcia-Constantino. "Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review." Symmetry 13, no. 2 (January 28, 2021): 214. http://dx.doi.org/10.3390/sym13020214.
Анотація:
Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined.
17
Bouayed, Jaouad, Hassan Rammal, and Rachid Soulimani. "Oxidative Stress and Anxiety: Relationship and Cellular Pathways." Oxidative Medicine and Cellular Longevity 2, no. 2 (2009): 63–67. http://dx.doi.org/10.4161/oxim.2.2.7944.
Анотація:
High O2consumption, modest antioxidant defenses and a lipid-rich constitution make the brain highly vulnerable to redox imbalances. Oxidative damage in the brain causes nervous system impairment. Recently, oxidative stress has also been implicated in depression, anxiety disorders and high anxiety levels. The findings which establish a link between oxidative stress and pathological anxiety have inspired a number of other recent studies focusing on the link between oxidative status and normal anxiety and also on a possible causal relationship between cellular oxidative stress and emotional stress. This review examines the recent discoveries made on the link between oxidative status and normal anxiety levels and the putative role of oxidative stress in genesis of anxiety. We discuss the different opinions and questions that exist in the field and review the methodological approaches that are being used to determine a causal relationship between oxidative and emotional stress.
18
Wahlang, Imayanmosha, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, and Elzbieta Jasinska. "Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age." Sensors 22, no. 5 (February 24, 2022): 1766. http://dx.doi.org/10.3390/s22051766.
Анотація:
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.
19
Foy, Judith G., Marissa Feldman, Edward Lin, Margaret Mahoney, and Chelsea Sjoblom. "Neuroscience Workshops for Fifth-Grade School Children by Undergraduate Students: A University–School Partnership." CBE—Life Sciences Education 5, no. 2 (June 2006): 128–36. http://dx.doi.org/10.1187/cbe.05-08-0107.
Анотація:
The National Science Education Standards recommend that science be taught using inquiry-based approaches. Inspired by the Dana Alliance for Brain Initiatives, we examined whether undergraduate students could learn how to conduct field research by teaching elementary school children basic neuroscience concepts in interactive workshops. In an inquiry-based learning experience of their own, undergraduate psychology students working under the close supervision of their instructor designed and provided free, interactive, hour-long workshops focusing on brain structure and function, brain damage and disorders, perception and illusions, and drugs and hormones to fifth-graders from diverse backgrounds, and we assessed the effectiveness of the workshops using a pretest–post-test design. The results suggest that the workshops enhanced the children's knowledge of neuroscience concepts as measured using pre- and post-open-ended assessments. The undergraduates also found their learning experience engaging and productive. The article includes detailed descriptions of the workshop activities, procedures, the course in which the undergraduates implemented the workshops, and guidance for future university–school collaborations aimed at enhancing science literacy.
20
Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.
Анотація:
Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.
21
Li, Qingkai, Yanbo Pang, Yushi Wang, Xinyu Han, Qing Li, and Mingguo Zhao. "CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm." Biomimetics 8, no. 5 (August 25, 2023): 389. http://dx.doi.org/10.3390/biomimetics8050389.
Анотація:
Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot’s operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.
22
Husbands, Phil, Yoonsik Shim, Michael Garvie, Alex Dewar, Norbert Domcsek, Paul Graham, James Knight, Thomas Nowotny, and Andrew Philippides. "Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics." Applied Intelligence 51, no. 9 (May 10, 2021): 6467–96. http://dx.doi.org/10.1007/s10489-021-02275-9.
Анотація:
AbstractThis paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies.
23
Mutkule, Prasad R., Nilesh P. Sable, Parikshit N. Mahalle, and Gitanjali R. Shinde. "Histopathological parameter and brain tumor mapping using distributed optimizer tuned explainable AI classifier." Journal of Autonomous Intelligence 7, no. 5 (May 21, 2024): 1617. http://dx.doi.org/10.32629/jai.v7i5.1617.
Анотація:
<p>Brain tumors represent a critical and severe challenge worldwide early and accurate diagnosis is necessary to increase the predictions for individuals with brain tumors. Several studies on brain tumor mapping have been conducted recently; however, the methods have some drawbacks, including poor image quality, a lack of data, and a limited capacity for generalization ability. To tackle these drawbacks this research presents a distributed optimizer tuned explainable AI classifier model for brain tumor mapping from histopathological images. The foraging gyps africanus optimization enabled explainable artificial intelligence (FGAO enabled explainable AI) combines the advantages of the explainable AI classifier model and hybrid spatio-temporal attention-based ResUNet segmentation model. The hybrid spatio-temporal attention-based ResUNet segmentation model accurately segments the histopathological images that leverage both Spatio-Temporal attention and the ResUNet model which addresses performance degradation problems. The nature-inspired algorithms draw inspiration from the foraging and hunting traits which optimize the tunable parameters of the explainable AI classifier. The SHAP model in the explainable AI translates the insights into predictions that produce explanations for the decisions made by the CNN model which fosters end-user confidence. The experimental results show that the FGAO-enabled explainable AI model outperforms the conventional approaches in terms of accuracy 95.75%, sensitivity 95.10%, and specificity 96.32% for TP 80.</p>
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Zhou, Rui, Ju Wang, Guijiang Xia, Jingyang Xing, Hongming Shen, and Xiaoyan Shen. "Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation." Entropy 26, no. 5 (April 30, 2024): 385. http://dx.doi.org/10.3390/e26050385.
Анотація:
In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.
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Díaz-Pernas, Francisco Javier, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez, and David González-Ortega. "A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network." Healthcare 9, no. 2 (February 2, 2021): 153. http://dx.doi.org/10.3390/healthcare9020153.
Анотація:
In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.
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Khan, Ameer Hamza, Xinwei Cao, Bin Xu, and Shuai Li. "Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System." Biomimetics 7, no. 3 (June 23, 2022): 84. http://dx.doi.org/10.3390/biomimetics7030084.
Анотація:
Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.
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Camuñas-Mesa, Luis, Bernabé Linares-Barranco, and Teresa Serrano-Gotarredona. "Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations." Materials 12, no. 17 (August 27, 2019): 2745. http://dx.doi.org/10.3390/ma12172745.
Анотація:
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.
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Riemer, Constanze, Michael Burwinkel, Anja Schwarz, Sandra Gültner, Simon W. F. Mok, Ines Heise, Nikola Holtkamp, and Michael Baier. "Evaluation of drugs for treatment of prion infections of the central nervous system." Journal of General Virology 89, no. 2 (February 1, 2008): 594–97. http://dx.doi.org/10.1099/vir.0.83281-0.
Анотація:
Prion diseases are fatal and at present there are neither cures nor therapies available to delay disease onset or progression in humans. Inspired in part by therapeutic approaches in the fields of Alzheimer's disease and amyotrophic lateral sclerosis, we tested five different drugs, which are known to efficiently pass through the blood–brain barrier, in a murine prion model. Groups of intracerebrally prion-challenged mice were treated with the drugs curcumin, dapsone, ibuprofen, memantine and minocycline. Treatment with antibiotics dapsone and minocycline had no therapeutic benefit. Ibuprofen-treated mice showed severe adverse effects, which prevented assessment of therapeutic efficacy. Mice treated with low- but not high-dose curcumin and mice treated with memantine survived infections significantly longer than untreated controls (P<0.01). These results encourage further research efforts to improve the therapeutic effect of these drugs.
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Tominov, Roman V., Zakhar E. Vakulov, Vadim I. Avilov, Ivan A. Shikhovtsov, Vadim I. Varganov, Victor B. Kazantsev, Lovi Raj Gupta, Chander Prakash, and Vladimir A. Smirnov. "Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications." Nanomaterials 13, no. 10 (May 9, 2023): 1583. http://dx.doi.org/10.3390/nano13101583.
Анотація:
This paper proposes two different approaches to studying resistive switching of oxide thin films using scratching probe nanolithography of atomic force microscopy (AFM). These approaches allow us to assess the effects of memristor size and top-contact thickness on resistive switching. For that purpose, we investigated scratching probe nanolithography regimes using the Taguchi method, which is known as a reliable method for improving the reliability of the result. The AFM parameters, including normal load, scratch distance, probe speed, and probe direction, are optimized on the photoresist thin film by the Taguchi method. As a result, the pinholes with diameter ranged from 25.4 ± 2.2 nm to 85.1 ± 6.3 nm, and the groove array with a depth of 40.5 ± 3.7 nm and a roughness at the bottom of less than a few nanometers was formed. Then, based on the Si/TiN/ZnO/photoresist structures, we fabricated and investigated memristors with different spot sizes and TiN top contact thickness. As a result, the HRS/LRS ratio, USET, and ILRS are well controlled for a memristor size from 27 nm to 83 nm and ranged from ~8 to ~128, from 1.4 ± 0.1 V to 1.8 ± 0.2 V, and from (1.7 ± 0.2) × 10−10 A to (4.2 ± 0.6) × 10−9 A, respectively. Furthermore, the HRS/LRS ratio and USET are well controlled at a TiN top contact thickness from 8.3 ± 1.1 nm to 32.4 ± 4.2 nm and ranged from ~22 to ~188 and from 1.15 ± 0.05 V to 1.62 ± 0.06 V, respectively. The results can be used in the engineering and manufacturing of memristive structures for neuromorphic applications of brain-inspired artificial intelligence systems.
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Purwono, Purwono, Agung Budi Prasetio, and Burhanuddin bin Mohd Aboobaider. "Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice." Journal of Advanced Health Informatics Research 1, no. 2 (September 23, 2023): 95–105. http://dx.doi.org/10.59247/jahir.v1i2.87.
Анотація:
Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended
31
Zhang, Cuiling. "Neuroscience and Translation." International Journal of Translation and Interpreting Research 15, no. 2 (July 31, 2023): 180–83. http://dx.doi.org/10.12807/ti.115202.2023.r02.
Анотація:
In the past decades, researchers have established various theories and approaches to explore the nature of translation, this “most complex type of event yet produced in the evolution of the cosmos” (Richard, 1953:250). Especially since the inception of Translation Studies as an academic discipline in the 1970s, translation scholars have drawn extensively on tools, concepts, and theories from other disciplines, such as sociology, anthropology, psychology, and biology in their efforts to explore the many facets of translation and interpreting. Now, neuroscience came to the fore. As the study of the nervous system, the task of neuroscience is to understand brain processes— how we perceive, act, learn, and remember – and explain behavior in terms of brain activities (Kandel et al., 2012, pp. 3-5). For decades, neuroscientists have explored human language and have produced remarkable studies on language development and learning. Yet the findings on how the brain handles language processing are still primarily based on monolinguals. The mental process of multilingual people and many other aspects of the transfer between different languages remain largely unsettled. This inspired Maria Tymoczko to explore the neurological mechanisms involved in translating, a field that she dubs as one of the “known unknowns” in translation studies (Tymoczko, 2012) and believes will fundamentally influence the way translation is thought about and ultimately illuminate many aspects of translation, including the “black box” of the individual translator.
32
Xu, Wentao, Sung-Yong Min, Hyunsang Hwang, and Tae-Woo Lee. "Organic core-sheath nanowire artificial synapses with femtojoule energy consumption." Science Advances 2, no. 6 (June 2016): e1501326. http://dx.doi.org/10.1126/sciadv.1501326.
Анотація:
Emulation of biological synapses is an important step toward construction of large-scale brain-inspired electronics. Despite remarkable progress in emulating synaptic functions, current synaptic devices still consume energy that is orders of magnitude greater than do biological synapses (~10 fJ per synaptic event). Reduction of energy consumption of artificial synapses remains a difficult challenge. We report organic nanowire (ONW) synaptic transistors (STs) that emulate the important working principles of a biological synapse. The ONWs emulate the morphology of nerve fibers. With a core-sheath–structured ONW active channel and a well-confined 300-nm channel length obtained using ONW lithography, ~1.23 fJ per synaptic event for individual ONW was attained, which rivals that of biological synapses. The ONW STs provide a significant step toward realizing low-energy–consuming artificial intelligent electronics and open new approaches to assembling soft neuromorphic systems with nanometer feature size.
33
Mehmood, Atif, Muazzam Maqsood, Muzaffar Bashir, and Yang Shuyuan. "A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease." Brain Sciences 10, no. 2 (February 5, 2020): 84. http://dx.doi.org/10.3390/brainsci10020084.
Анотація:
Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
34
Tang, Jing. "Shining Light on the Nervous System: From Biomaterials to Bioelectronics." ECS Meeting Abstracts MA2019-02, no. 55 (September 1, 2019): 2421. http://dx.doi.org/10.1149/ma2019-02/55/2421.
Анотація:
The dichotomy between advanced materials and brain has driven the curiosity of scientists to explore the wonders of the brain, as well as motivated the continued innovations of novel technologies based on advances in materials science and engineering to understand the brain. To improve treatments of brain-related diseases will require new tools and methods to map and to repair the brain with precision and biocompatibility. Current treatments of pain heavily rely on opioids, resulting in significant side effects such as addiction, tolerance, leading to the Opioid Overdose Crisis as we know of today. Smart drug delivery systems may provide an effective solution. Here I present the development of polymer-based externally-triggerable drug delivery systems for on-demand, repeatable and adjustable local anesthesia, where the timing, duration, and intensity of nerve block can be controlled through external energy triggers such as light. In addition to the new pharmacological approaches, bioelectronic platforms to enhance our insights into the eye and will also be discussed. The restoration of light response with complex spatiotemporal features in retinal degenerative diseases towards retinal prosthesis has proven to be a considerable challenge over the past decades. Herein, inspired by the structure and function of photoreceptors in retinas, I develop artificial retina based on gold nanoparticle-decorated titania nanowire arrays, for restoration of visual responses in the blind mice with degenerated photoreceptors. Green, blue and near UV light responses in the retinal ganglion cells (RGCs) are restored with a spatial resolution better than 100 µm. ON responses in RGCs are blocked by glutamatergic antagonists, suggesting functional preservation of the remaining retinal circuits. Moreover, neurons in the primary visual cortex respond to light after subretinal implant of nanowire arrays. Improvement in pupillary light reflex suggests the behavioral recovery of light sensitivity. My study will shed light on the development of a new generation of optoelectronic toolkits for subretinal prosthetic devices. Through pharmacological, optical, and electrical toolsets, I aim to develop effective therapeutic solutions to neurological disease states. These results, along with a discussion of future neural interfaces, aim to improve our understanding of the nervous system and to inform new therapeutic approaches for biomaterials and bioelectronics.
35
Mansouri-Benssassi, Esma, and Juan Ye. "Synch-Graph: Multisensory Emotion Recognition Through Neural Synchrony via Graph Convolutional Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1351–58. http://dx.doi.org/10.1609/aaai.v34i02.5491.
Анотація:
Human emotions are essentially multisensory, where emotional states are conveyed through multiple modalities such as facial expression, body language, and non-verbal and verbal signals. Therefore having multimodal or multisensory learning is crucial for recognising emotions and interpreting social signals. Existing multisensory emotion recognition approaches focus on extracting features on each modality, while ignoring the importance of constant interaction and co-learning between modalities. In this paper, we present a novel bio-inspired approach based on neural synchrony in audio-visual multisensory integration in the brain, named Synch-Graph. We model multisensory interaction using spiking neural networks (SNN) and explore the use of Graph Convolutional Networks (GCN) to represent and learn neural synchrony patterns. We hypothesise that modelling interactions between modalities will improve the accuracy of emotion recognition. We have evaluated Synch-Graph on two state-of-the-art datasets and achieved an overall accuracy of 98.3% and 96.82%, which are significantly higher than the existing techniques.
36
Abbas, Haider, Jiayi Li, and Diing Shenp Ang. "Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications." Micromachines 13, no. 5 (April 30, 2022): 725. http://dx.doi.org/10.3390/mi13050725.
Анотація:
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.
37
Sarangi, Biswaranjan, Arunanshu Mahapatro, and Biswajit Tripathy. "Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network." International Journal of Business Data Communications and Networking 17, no. 2 (July 2021): 1–16. http://dx.doi.org/10.4018/ijbdcn.286705.
Анотація:
Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.
38
Huerta, Ramón, Shankar Vembu, José M. Amigó, Thomas Nowotny, and Charles Elkan. "Inhibition in Multiclass Classification." Neural Computation 24, no. 9 (September 2012): 2473–507. http://dx.doi.org/10.1162/neco_a_00321.
Анотація:
The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches.
39
Mahmoud, Ahmed, and Mohamed Atia. "Improved Visual SLAM Using Semantic Segmentation and Layout Estimation." Robotics 11, no. 5 (September 6, 2022): 91. http://dx.doi.org/10.3390/robotics11050091.
Анотація:
The technological advances in computational systems have enabled very complex computer vision and machine learning approaches to perform efficiently and accurately. These new approaches can be considered a new set of tools to reshape the visual SLAM solutions. We present an investigation of the latest neuroscientific research that explains how the human brain can accurately navigate and map unknown environments. The accuracy suggests that human navigation is not affected by traditional visual odometry drifts resulting from tracking visual features. It utilises the geometrical structures of the surrounding objects within the navigated space. The identified objects and space geometrical shapes anchor the estimated space representation and mitigate the overall drift. Inspired by the human brain’s navigation techniques, this paper presents our efforts to incorporate two machine learning techniques into a VSLAM solution: semantic segmentation and layout estimation to imitate human abilities to map new environments. The proposed system benefits from the geometrical relations between the corner points of the cuboid environments to improve the accuracy of trajectory estimation. Moreover, the implemented SLAM solution semantically groups the map points and then tracks each group independently to limit the system drift. The implemented solution yielded higher trajectory accuracy and immunity to large pure rotations.
40
Spencer, Ana P., Marília Torrado, Beatriz Custódio, Sara C. Silva-Reis, Sofia D. Santos, Victoria Leiro, and Ana P. Pêgo. "Breaking Barriers: Bioinspired Strategies for Targeted Neuronal Delivery to the Central Nervous System." Pharmaceutics 12, no. 2 (February 23, 2020): 192. http://dx.doi.org/10.3390/pharmaceutics12020192.
Анотація:
Central nervous system (CNS) disorders encompass a vast spectrum of pathological conditions and represent a growing concern worldwide. Despite the high social and clinical interest in trying to solve these pathologies, there are many challenges to bridge in order to achieve an effective therapy. One of the main obstacles to advancements in this field that has hampered many of the therapeutic strategies proposed to date is the presence of the CNS barriers that restrict the access to the brain. However, adequate brain biodistribution and neuronal cells specific accumulation in the targeted site also represent major hurdles to the attainment of a successful CNS treatment. Over the last few years, nanotechnology has taken a step forward towards the development of therapeutics in neurologic diseases and different approaches have been developed to surpass these obstacles. The versatility of the designed nanocarriers in terms of physical and chemical properties, and the possibility to functionalize them with specific moieties, have resulted in improved neurotargeted delivery profiles. With the concomitant progress in biology research, many of these strategies have been inspired by nature and have taken advantage of physiological processes to achieve brain delivery. Here, the different nanosystems and targeting moieties used to achieve a neuronal delivery reported in the open literature are comprehensively reviewed and critically discussed, with emphasis on the most recent bioinspired advances in the field. Finally, we express our view on the paramount challenges in targeted neuronal delivery that need to be overcome for these promising therapeutics to move from the bench to the bedside.
41
Stippler, Martina, Veronica Ortiz, P. David Adelson, Yue-Fang Chang, Elizabeth C. Tyler-Kabara, Stephen R. Wisniewski, Ericka L. Fink, Patrick M. Kochanek, S. Danielle Brown, and Michael J. Bell. "Brain tissue oxygen monitoring after severe traumatic brain injury in children: relationship to outcome and association with other clinical parameters." Journal of Neurosurgery: Pediatrics 10, no. 5 (November 2012): 383–91. http://dx.doi.org/10.3171/2012.8.peds12165.
Анотація:
Object Minimizing secondary brain injuries after traumatic brain injury (TBI) in children is critical to maximizing neurological outcome. Brain tissue oxygenation monitoring (as measured by interstitial partial pressure of O2 [PbO2]) is a new tool that may aid in guiding therapies, yet experience in children is limited. This study aims to describe the authors' experience of PbO2 monitoring after TBI. It was hypothesized that PbO2 thresholds could be established that were associated with favorable neurological outcome, and it was determined whether any relationships between PbO2 and other important clinical variables existed. Methods Forty-six children with severe TBI (Glasgow Coma Scale score ≤ 8 after resuscitation) who underwent PbO2 and brain temperature monitoring between September 2004 and June 2008 were studied. All patients received standard neurocritical care, and 24 were concurrently enrolled in a trial of therapeutic early hypothermia (n = 12/group). The PbO2 was measured in the uninjured frontal cortex. Hourly recordings and calculated daily means of various variables including PbO2, intracranial pressure (ICP), cerebral perfusion pressure (CPP), mean arterial blood pressure, partial pressure of arterial O2, and fraction of inspired O2 were compared using several statistical approaches. Glasgow Outcome Scale scores were determined at 6 months after injury. Results The mean patient age was 9.4 years (range 0.1–16.5 years; 13 girls) and 8554 hours of monitoring were analyzed (PbO2 range 0.0–97.2 mm Hg). A PbO2 of 30 mm Hg was associated with the highest sensitivity/specificity for favorable neurological outcome at 6 months after TBI, yet CPP was the only factor that was independently associated with favorable outcome. Surprisingly, instances of preserved PbO2 with altered ICP and CPP were observed in some children with unfavorable outcomes. Conclusions Monitoring of PbO2 demonstrated complex interactions with clinical variables reflecting intracranial dynamics using this protocol. A higher threshold than reported in studies in adults was suggested as a potential therapeutic target, but this threshold was not associated with improved outcomes. Additional studies to assess the utility of PbO2 monitoring after TBI in children are needed.
42
Jabir, Brahim, and Noureddine Falih. "Dropout, a basic and effective regularization method for a deep learning model: a case study." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 1009. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp1009-1016.
Анотація:
Deep learning is based on a network of artificial neurons inspired by the human brain. This network is made up of tens or even hundreds of "layers" of neurons. The fields of application of deep learning are indeed multiple; Agriculture is one of those fields in which deep learning is used in various agricultural problems (disease detection, pest detection, and weed identification). A major problem with deep learning is how to create a model that works well, not only on the learning set but also on the validation set. Many approaches used in neural networks are explicitly designed to reduce overfit, possibly at the expense of increasing validation accuracy and training accuracy. In this paper, a basic technique (dropout) is proposed to minimize overfit, we integrated it into a convolutional neural network model to classify weed species and see how it impacts performance, a complementary solution (exponential linear units) are proposed to optimize the obtained results. The results showed that these proposed solutions are practical and highly accurate, enabling us to adopt them in deep learning models.
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Carriero, Alessandro, Léon Groenhoff, Elizaveta Vologina, Paola Basile, and Marco Albera. "Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024." Diagnostics 14, no. 8 (April 19, 2024): 848. http://dx.doi.org/10.3390/diagnostics14080848.
Анотація:
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
44
Beyan, Cigdem, and Howard I. Browman. "Setting the stage for the machine intelligence era in marine science." ICES Journal of Marine Science 77, no. 4 (June 18, 2020): 1267–73. http://dx.doi.org/10.1093/icesjms/fsaa084.
Анотація:
Abstract Machine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated.
45
Bernert, Marie, and Blaise Yvert. "An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting." International Journal of Neural Systems 29, no. 08 (September 25, 2019): 1850059. http://dx.doi.org/10.1142/s0129065718500594.
Анотація:
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.
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Li, Shangjin, and Yijun Zhao. "Addressing Motion Blurs in Brain MRI Scans Using Conditional Adversarial Networks and Simulated Curvilinear Motions." Journal of Imaging 8, no. 4 (March 23, 2022): 84. http://dx.doi.org/10.3390/jimaging8040084.
Анотація:
In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifacts in MRI scans. Inspired by the recent success of deep learning models in medical image analysis, we investigate the efficacy of employing generative adversarial networks (GANs) to address motion blurs in brain MRI scans. We cast the problem as a blind deconvolution task where a neural network is trained to guess a blurring kernel that produced the observed corruption. Specifically, our study explores a new approach under the sparse coding paradigm where every ground truth corrupting kernel is assumed to be a “combination” of a relatively small universe of “basis” kernels. This assumption is based on the intuition that, on small distance scales, patients’ moves follow simple curves and that complex motions can be obtained by combining a number of simple ones. We show that, with a suitably dense basis, a neural network can effectively guess the degrading kernel and reverse some of the damage in the motion-affected real-world scans. To this end, we generated 10,000 continuous and curvilinear kernels in random positions and directions that are likely to uniformly populate the space of corrupting kernels in real-world scans. We further generated a large dataset of 225,000 pairs of sharp and blurred MR images to facilitate training effective deep learning models. Our experimental results demonstrate the viability of the proposed approach evaluated using synthetic and real-world MRI scans. Our study further suggests there is merit in exploring separate models for the sagittal, axial, and coronal planes.
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Gabashvili, Anna N., Nelly S. Chmelyuk, Vera V. Oda, Maria K. Leonova, Viktoria A. Sarkisova, Polina A. Lazareva, Alevtina S. Semkina, Nikolai A. Belyakov, Timur R. Nizamov, and Petr I. Nikitin. "Magnetic and Fluorescent Dual-Labeled Genetically Encoded Targeted Nanoparticles for Malignant Glioma Cell Tracking and Drug Delivery." Pharmaceutics 15, no. 10 (October 4, 2023): 2422. http://dx.doi.org/10.3390/pharmaceutics15102422.
Анотація:
Human glioblastoma multiforme (GBM) is a primary malignant brain tumor, a radically incurable disease characterized by rapid growth resistance to classical therapies, with a median patient survival of about 15 months. For decades, a plethora of approaches have been developed to make GBM therapy more precise and improve the diagnosis of this pathology. Targeted delivery mediated by the use of various molecules (monoclonal antibodies, ligands to overexpressed tumor receptors) is one of the promising methods to achieve this goal. Here we present a novel genetically encoded nanoscale dual-labeled system based on Quasibacillus thermotolerans (Qt) encapsulins exploiting biologically inspired designs with iron-containing nanoparticles as a cargo, conjugated with human fluorescent labeled transferrin (Tf) acting as a vector. It is known that the expression of transferrin receptors (TfR) in glioma cells is significantly higher compared to non-tumor cells, which enables the targeting of the resulting nanocarrier. The selectivity of binding of the obtained nanosystem to glioma cells was studied by qualitative and quantitative assessment of the accumulation of intracellular iron, as well as by magnetic particle quantification method and laser scanning confocal microscopy. Used approaches unambiguously demonstrated that transferrin-conjugated encapsulins were captured by glioma cells much more efficiently than by benign cells. The resulting bioinspired nanoplatform can be supplemented with a chemotherapeutic drug or genotherapeutic agent and used for targeted delivery of a therapeutic agent to malignant glioma cells. Additionally, the observed cell-assisted biosynthesis of magnetic nanoparticles could be an attractive way to achieve a narrow size distribution of particles for various applications.
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Eckerdt, Frank, and Leonidas C. Platanias. "Emerging Role of Glioma Stem Cells in Mechanisms of Therapy Resistance." Cancers 15, no. 13 (July 1, 2023): 3458. http://dx.doi.org/10.3390/cancers15133458.
Анотація:
Since their discovery at the beginning of this millennium, glioma stem cells (GSCs) have sparked extensive research and an energetic scientific debate about their contribution to glioblastoma (GBM) initiation, progression, relapse, and resistance. Different molecular subtypes of GBM coexist within the same tumor, and they display differential sensitivity to chemotherapy. GSCs contribute to tumor heterogeneity and recapitulate pathway alterations described for the three GBM subtypes found in patients. GSCs show a high degree of plasticity, allowing for interconversion between different molecular GBM subtypes, with distinct proliferative potential, and different degrees of self-renewal and differentiation. This high degree of plasticity permits adaptation to the environmental changes introduced by chemo- and radiation therapy. Evidence from mouse models indicates that GSCs repopulate brain tumors after therapeutic intervention, and due to GSC plasticity, they reconstitute heterogeneity in recurrent tumors. GSCs are also inherently resilient to standard-of-care therapy, and mechanisms of resistance include enhanced DNA damage repair, MGMT promoter demethylation, autophagy, impaired induction of apoptosis, metabolic adaptation, chemoresistance, and immune evasion. The remarkable oncogenic properties of GSCs have inspired considerable interest in better understanding GSC biology and functions, as they might represent attractive targets to advance the currently limited therapeutic options for GBM patients. This has raised expectations for the development of novel targeted therapeutic approaches, including targeting GSC plasticity, chimeric antigen receptor T (CAR T) cells, and oncolytic viruses. In this review, we focus on the role of GSCs as drivers of GBM and therapy resistance, and we discuss how insights into GSC biology and plasticity might advance GSC-directed curative approaches.
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Alongi, Pierpaolo, Annachiara Arnone, Viola Vultaggio, Alessandro Fraternali, Annibale Versari, Cecilia Casali, Gaspare Arnone, Francesco DiMeco, and Ignazio Gaspare Vetrano. "Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review." Cancers 16, no. 2 (January 18, 2024): 407. http://dx.doi.org/10.3390/cancers16020407.
Анотація:
The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).
50
Puerto, Eduard, Jose Aguilar, and Angel Pinto. "Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model." Computers 13, no. 3 (March 12, 2024): 76. http://dx.doi.org/10.3390/computers13030076.
Анотація:
Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.