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Готові списки джерел за темами / Brain-inspired approaches

Добірка наукової літератури з теми "Brain-inspired approaches"

Автор: Grafiati

Опубліковано: 22 червня 2024

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Статті в журналах з теми "Brain-inspired approaches":

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

8

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.

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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.

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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.

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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.

Більше джерел

Дисертації з теми "Brain-inspired approaches":

1

da, Silva Gomes Joao Paulo. "Brain inspired approach to computational face recognition." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3544.

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Face recognition that is invariant to pose and illumination is a problem solved effortlessly by the human brain, but the computational details that underlie such efficient recognition are still far from clear. This thesis draws on research from psychology and neuroscience about face and object recognition and the visual system in order to develop a novel computational method for face detection, feature selection and representation, and memory structure for recall. A biologically plausible framework for developing a face recognition system will be presented. This framework can be divided into four parts: 1) A face detection system. This is an improved version of a biologically inspired feedforward neural network that has modifiable connections and reflects the hierarchical and elastic structure of the visual system. The face detection system can detect if a face is present in an input image, and determine the region which contains that face. The system is also capable of detecting the pose of the face. 2) A face region selection mechanism. This mechanism is used to determine the Gabor-style features corresponding to the detected face, i.e., the features from the region of interest. This region of interest is selected using a feedback mechanism that connects the higher level layer of the feedforward neural network where ultimately the face is detected to an intermediate level where the Gabor style features are detected. 3) A face recognition system which is based on the binary encoding of the Gabor style features selected to represent a face. Two alternative coding schemes are presented, using 2 and 4 bits to represent a winning orientation at each location. The effectiveness of the Gabor-style features and the different coding schemes in discriminating faces from different classes is evaluated using the Yale B Face Database. The results from this evaluation show that this representation is close to other results on the same database. 4) A theoretical approach for a memory system capable of memorising sequences of poses. A basic network for memorisation and recall of sequences of labels have been implemented, and from this it is extrapolated a memory model that could use the ability of this model to memorise and recall sequences, to assist in the recognition of faces by memorising sequences of poses. Finally, the capabilities of the detection and recognition parts of the system are demonstrated using a demo application that can learn and recognise faces from a webcam.

2

Muliukov, Artem. "Étude croisée des cartes auto-organisatrices et des réseaux de neurones profonds pour l'apprentissage multimodal inspiré du cerveau." Electronic Thesis or Diss., Université Côte d'Azur, 2024. https://intranet-theses.unice.fr/2024COAZ4008.

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La plasticité corticale est l'une des principales caractéristiques qui permettent à notre capacité d'apprendre et de s'adapter à notre environnement. En effet, le cortex cérébral a la capacité de s'auto-organiser à travers deux formes distinctes de plasticité: la plasticité structurelle et la plasticité synaptique. Ces mécanismes sont très probablement à la base d'une caractéristique extrêmement intéressante du développement du cerveau humain: l'association multimodale. Le cerveau utilise des corrélations spatio-temporelles entre plusieurs modalités pour structurer les données et créer du sens à partir des observations. De plus, les observations biologiques montrent qu'une modalité peut activer la représentation interne d'une autre modalité lorsque les deux sont corrélées. Pour modéliser un tel comportement, Edelman et Damasio ont proposé respectivement les cadres Reentry et Convergence Divergence Zone où les communications neuronales bidirectionnelles peuvent conduire à la fois à la fusion multimodale (convergence) et à l'activation intermodale (divergence). Néanmoins, ces frameworks ne fournissent pas de modèle de calcul au niveau neuronal, et seuls quelques travaux abordent cette question d'association multimodale bio-inspirée qui est pourtant nécessaire pour une représentation complète de l'environnement notamment en ciblant des systèmes intelligents autonomes et embarqués. Dans ce projet de doctorat, nous proposons de poursuivre l'exploration de modèles informatiques d'auto-organisation inspirés du cerveau pour l'apprentissage multimodal non supervisé dans les systèmes neuromorphiques. Ces architectures neuromorphes tirent leur efficacité énergétique des modèles bio-inspirés qu'elles supportent, et pour cette raison nous ne considérons dans notre travail que des règles d'apprentissage basées sur des traitements locaux et distribués
Cortical plasticity is one of the main features that enable our capability to learn and adapt in our environment. Indeed, the cerebral cortex has the ability to self-organize itself through two distinct forms of plasticity: the structural plasticity and the synaptic plasticity. These mechanisms are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. The brain uses spatio-temporal correlations between several modalities to structure the data and create sense from observations. Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated. To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). Nevertheless, these frameworks do not provide a computational model at the neuron level, and only few works tackle this issue of bio-inspired multimodal association which is yet necessary for a complete representation of the environment especially when targeting autonomous and embedded intelligent systems. In this doctoral project, we propose to pursue the exploration of brain-inspired computational models of self-organization for multimodal unsupervised learning in neuromorphic systems. These neuromorphic architectures get their energy-efficient from the bio-inspired models they support, and for that reason we only consider in our work learning rules based on local and distributed processing

3

(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices." 2019.

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Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks.

Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.

We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver.

Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.

Книги з теми "Brain-inspired approaches":

1

Lewin-Benham, Ann. Infants and toddlers at work: Using Reggio-inspired materials to support brain development. New York: Teachers College Press, 2010.

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2

Lewin-Benham, Ann. Infants and toddlers at work: Using Reggio-inspired materials to support brain development. New York: Teachers College Press, 2010.

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3

Cangelosi, Angelo, and Minoru Asada, eds. Cognitive Robotics. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13780.001.0001.

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The current state of the art in cognitive robotics, covering the challenges of building AI-powered intelligent robots inspired by natural cognitive systems. A novel approach to building AI-powered intelligent robots takes inspiration from the way natural cognitive systems—in humans, animals, and biological systems—develop intelligence by exploiting the full power of interactions between body and brain, the physical and social environment in which they live, and phylogenetic, developmental, and learning dynamics. This volume reports on the current state of the art in cognitive robotics, offering the first comprehensive coverage of building robots inspired by natural cognitive systems. Contributors first provide a systematic definition of cognitive robotics and a history of developments in the field. They describe in detail five main approaches: developmental, neuro, evolutionary, swarm, and soft robotics. They go on to consider methodologies and concepts, treating topics that include commonly used cognitive robotics platforms and robot simulators, biomimetic skin as an example of a hardware-based approach, machine-learning methods, and cognitive architecture. Finally, they cover the behavioral and cognitive capabilities of a variety of models, experiments, and applications, looking at issues that range from intrinsic motivation and perception to robot consciousness. Cognitive Robotics is aimed at an interdisciplinary audience, balancing technical details and examples for the computational reader with theoretical and experimental findings for the empirical scientist.

4

Paul, Sharon J. Art & Science in the Choral Rehearsal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190863760.001.0001.

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In recent decades, cognitive neuroscience research has increased our understanding of how the brain learns, retains, and recalls information. At the same time, social psychology researchers have developed insights into group dynamics, exploring what motivates individuals in a group to give their full effort, or conversely, what might instead inspire them to become freeloaders. This book explores the idea that choral conductors who better understand how the brain learns, and how individuals within groups function, can lead more efficient, productive, and enjoyable rehearsals. Armed with this knowledge, conductors can create rehearsal techniques which take advantage of certain fundamental brain and social psychology principles. Through such approaches, singers will become increasingly engaged physically and mentally in the rehearsal process. This book draws from a range of scientific studies to suggest and encourage effective, evidence-based techniques, and can help serve to reset and inspire new approaches toward teaching. Each chapter outlines exercises and creative ideas for conductors and music teachers, including the importance of embedding problem solving into rehearsal, the use of multiple entry points for newly acquired information, techniques to encourage an emotional connection to the music, and ways to incorporate writing exercises into rehearsal. Additional topics include brain-compatible teaching strategies to complement thorough score study, the science behind motivation, the role imagination plays in teaching, the psychology of rehearsal, and conducting tips and advice. All of these brain-friendly strategies serve to encourage singers’ active participation in rehearsals, with the goal of motivating beautiful, inspired, and memorable performances.

5

Tousi, Babak. Cognitive Enhancement in Non-Alzheimer’s Dementias. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190214401.003.0004.

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Cognitive enhancement in non-Alzheimer’s dementias has not been studied as extensively as that in Alzheimer’s dementia. This chapter reviews the research on cognitive enhancement for three types of dementia: vascular dementia, dementia with Lewy bodies, and frontal lobe dementia. The chapter reviews both pharmacological and nonpharmacological approaches for treatment of dementia. The focus is on randomized controlled trials for currently available medications, specifically cholinesterase inhibitors and memantine. Major advances in physical and cognitive rehabilitation during the past decade have inspired clinicians and researchers to explore the role of potential cognitive enhancers in different types of dementias. This chapter also examines the effects of therapeutic interventions such as exercise, physical rehabilitation, cognitive rehabilitation, and electrical stimulation of the brain on cognition in non-Alzheimer’s dementias.

6

Temkin, Larry S. Being Good in a World of Need. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849977.001.0001.

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Ours is a rich world filled with misery. This gives rise to a pressing question: how should the well-off respond to the needy? Peter Singer famously argued that just as we have an obligation to save a drowning child, we have an obligation to support charities like Oxfam. Inspired by Singer, Effective Altruism holds that we ought to support those charities doing the most good. Being Good in a World of Need powerfully challenges these views. Drawing on many sources, Temkin illustrates many disanalogies between saving a drowning child and supporting international charities, involving: intervening agents; effects of one’s actions; corruption; responsibility; accidents versus injustice; and aid beneficiaries. These disanalogies raise complex issues requiring a pluralistic approach, rather than Effective Altruism’s monistic, “do the most good” approach. Being Good discusses: ways aid may reward corrupt leaders and incentivize disastrous policies; charities ignoring or covering up negative impacts; the ethical disaster of aid efforts in Goma; brain and character drains; difficulties in replicability or scaling up model aid projects; ethical imperialism, paternalism, autonomy, and respect; Angus Deaton’s contention that aid undermines government responsiveness; Jeffrey Sachs and the Millennium Villages Project; conflicts between individual and collective morality; fairness and responsibility; focusing on badly off people rather than countries; humanitarian versus development aid; and ways of aiding other than on-the-ground charities. Being Good reinforces Temkin’s longstanding view that, morally, the well-off can’t ignore the needy. Unfortunately, what one should do given that truth is much more complex, and murky, than most have realized.

Частини книг з теми "Brain-inspired approaches":

1

Xie, Guoliang, Jinchang Ren, Huimin Zhao, Sophia Zhao, and Stephen Marshall. "Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis." In Advances in Brain Inspired Cognitive Systems, 342–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_33.

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2

Hussien, Intisar O., Kia Dashtipour, and Amir Hussain. "Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect." In Advances in Brain Inspired Cognitive Systems, 615–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00563-4_60.

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3

Minhas, Saliha, Soujanya Poria, Amir Hussain, and Khalid Hussainey. "A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure." In Advances in Brain Inspired Cognitive Systems, 317–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_36.

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4

Deussen, Erik, Herwig Unger, and Mario M. Kubek. "Brain-Inspired Approaches to Natural Language Processing and Explainable Artificial Intelligence." In Innovations for Community Services, 6–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06668-9_2.

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5

Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science, 105–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_8.

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AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

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Pfeifer, Rolf. "Morphological Computation: Connecting Brain, Body, and Environment." In Biologically Inspired Approaches to Advanced Information Technology, 2–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11613022_2.

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Abel, Andrew, Ricard Marxer, Jon Barker, Roger Watt, Bill Whitmer, Peter Derleth, and Amir Hussain. "A Data Driven Approach to Audiovisual Speech Mapping." In Advances in Brain Inspired Cognitive Systems, 331–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49685-6_30.

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Chen, Zengqiang, Yuefei Wei, and Qinglin Sun. "An Improved Free Search Approach for Energy Optimization in Wireless Sensor Networks." In Advances in Brain Inspired Cognitive Systems, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_2.

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Xiao, Yun, Fei Liu, Yabin Zhu, Chenglong Li, Futian Wang, and Jin Tang. "UAV Cross-Modal Image Registration: Large-Scale Dataset and Transformer-Based Approach." In Advances in Brain Inspired Cognitive Systems, 166–76. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1417-9_16.

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Çakır, Murat Perit, Abdullah Murat Şenyiğit, Daryal Murat Akay, Hasan Ayaz, and Veysi İşler. "Evaluation of UAS Camera Operator Interfaces in a Simulated Task Environment: An Optical Brain Imaging Approach." In Advances in Brain Inspired Cognitive Systems, 62–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31561-9_7.

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Тези доповідей конференцій з теми "Brain-inspired approaches":

1

Romero, J. A., L. A. Diago, J. Shinoda, and I. Hagiwara. "Evaluation of Brain Models to Control a Robotic Origami Arm Using Holographic Neural Networks." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-48074.

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In robotics, one of the most difficult task is to perform a precisely and fast movement of a robotic arm. For paper-folding robots, it is still extremely difficult to execute the required manipulations of the paper mainly because the difficulties in modeling and control of the paper. In this paper two control models are proposed to solve this problem. One of the best approaches comes from Neuroscience, where using a human’s brain inspired control system known as Cerebellar control model (CCM), precisely and fast movements of a robotic arm can be performed. In the CCM a Feedback controller motor command is used as a target signal to train an Artificial Neural Network (NN), and use the output of the NN as a Feed-forward signal. In this paper two training methods were evaluated in order to improve the behavior in CCM: the traditional Back propagation and a Holographic method.

2

Wang, Fangzhen, Rong Wu, and Yan Fang. "Nanomedicine photoluminescence crystal-inspired brain sensing approach." In Neural Imaging and Sensing 2018, edited by Qingming Luo and Jun Ding. SPIE, 2018. http://dx.doi.org/10.1117/12.2289891.

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3

Zhang, Tielin, Yi Zeng, Dongcheng Zhao, and Bo Xu. "Brain-inspired Balanced Tuning for Spiking Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/229.

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Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible to be directly tuned by back propagation. Here we propose an alternative biological inspired balanced tuning approach to train SNNs. The approach contains three main inspirations from the brain: Firstly, the biological network will usually be trained towards the state where the temporal update of variables are equilibrium (e.g. membrane potential); Secondly, specific proportions of excitatory and inhibitory neurons usually contribute to stable representations; Thirdly, the short-term plasticity (STP) is a general principle to keep the input and output of synapses balanced towards a better learning convergence. With these inspirations, we train SNNs with three steps: Firstly, the SNN model is trained with three brain-inspired principles; then weakly supervised learning is used to tune the membrane potential in the final layer for network classification; finally the learned information is consolidated from membrane potential into the weights of synapses by Spike-Timing Dependent Plasticity (STDP). The proposed approach is verified on the MNIST hand-written digit recognition dataset and the performance (the accuracy of 98.64%) indicates that the ideas of balancing state could indeed improve the learning ability of SNNs, which shows the power of proposed brain-inspired approach on the tuning of biological plausible SNNs.

4

Bienstman, Peter, Joni Dambre, Andrew Katumba, Matthias Freiberger, Floris Laporte, and Alessio Lugnan. "Photonic reservoir computing: a brain-inspired approach for information processing." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/ofc.2018.m4f.4.

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5

Zhang, Boyuan, Shuyuan Zhu, Tong Xie, Xibang Yang, Yahui Liu, and Bing Zeng. "Filamentary Convolution for Spoken Language Identification: A Brain-Inspired Approach." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446318.

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6

H. Hamid, Oussama, and Jochen Braun. "Attractor Neural States: A Brain-Inspired Complementary Approach to Reinforcement Learning." In 9th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006580203850392.

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Parsa, Maryam, Khaled N. Khasawneh, and Ihsen Alouani. "A Brain-inspired Approach for Malware Detection using Sub-semantic Hardware Features." In GLSVLSI '23: Great Lakes Symposium on VLSI 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583781.3590293.

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Yang, Erfu, Amir Hussain, and Kevin Gurney. "A brain-inspired soft switching approach: towards a cognitive cruise control system." In International Conference on Control Engineering and Electronics Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/cceee140071.

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Beiu, Valeriu, Basheer A. M. Madappuram, and Martin McGinnity. "On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -." In 2008 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). IEEE, 2008. http://dx.doi.org/10.1109/icsamos.2008.4664844.

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Caversan, Fabio, Lucas Rodrigues, Geanderson Ferreira, Danilo De Almeida, Carlos Veber, Gabriel Dias Rezende Martins, Dr Nabih Jaber, and Dr George Pappas. "Towards a Neuro-Symbolic Approach to Bridge the Gap Between Brain and Mind-Inspired Models." In 6th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2023. http://dx.doi.org/10.46254/eu6.20230441.

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Звіти організацій з теми "Brain-inspired approaches":

1

Li, Xiao, GX Xu, FY Ling, ZH Yin, Y. Wei,, Y. Zhao, Xn Li, WC Qi, L. Zhao, and FR Liang. The dose-effect association between electroacupuncture sessions and its effect on chronic migraine: a protocol of a meta-regression of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0085.

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Review question / Objective: We will use a meta-regression approach to verify the dose-effect relationship between the number of electroacupuncture sessions and its effects on migraine. Condition being studied: Migraine is recurrent and chronic, requiring long-term control, but the side effects caused by long-term use limit the use of pharmacotherapy, like non-steroidal anti-inflammatory drugs (NSAIDS), ergoamines and opioids. With fewer side effects and lower cost, acupuncture is becoming a more attractive option for migraine. Relevant studies have confirmed the clinical effects of electroacupuncture on migraine and its effects on intracranial blood flow velocity, functional brain imaging and neuroinflammation. However, uncertainty exists regarding the dose-effect between electroacupuncture and migraine. In recent years, inspired by the dose-effect researches in pharmacology and epidemiology, researches focusing on the dose-effect association between acupuncture and diseases has also begun to emerge. So in this protocol, we designed to use a meta-regression approach to explore the optimal electroacupuncture dose for migraine.