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Suggested Topics within your search.
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Brain development during the lifespan of cynomolgus monkeys
Published 2025-01-01“…The cynomolgus monkey has emerged as a predominant nonhuman primate model in neuroscience research. A comprehensive grasp of the growth and development patterns in the cynomolgus monkey brain is crucial for the judicious utilization of this species in neuroscience investigations. …”
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Beyond sensitivity: what are the enabling opportunities of OPM-MEG?
Published 2025-01-01“…This is critical in adoption of naturalistic paradigms that move beyond “laboratory neuroscience” toward “real world neuroscience”. It is also critically important in application to pediatric populations who cannot or will not remain still during conventional MEG scan procedures. …”
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Neurological Impairment and Literary Empowerment in Nicole Krauss’s Man Walks into a Room
Published 2021-12-01“…While she draws on neuroscience, she also writes against it, as she emphasizes the explanatory gap and the problem of qualia. …”
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Electricity, Neurology, and Noninvasive Brain Stimulation: Looking Back, Looking Ahead
Published 2020-01-01“…This article looks at their origins and their journey into noninvasive brain stimulation technique of transcranial direct current stimulation (tDCS), which is now popular in neuroscience research.…”
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EEG as a neural measure of hypoxia-related impairment
Published 2025-02-01“…Recent advances in neuroscience have permitted non-invasive observation of neural activity under controlled hypoxia exposures and have begun to uncover how the brain responds to hypoxia. …”
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The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities
Published 2024-11-01“…Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.…”
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An intein-split transactivator for intersectional neural imaging and optogenetic manipulation
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Nanotechnology-driven electrochemical neurotransmitter sensing as a fundamental approach towards improving diagnostics and therapeutics: A review
Published 2025-06-01“…This review additionally looks at the progress accomplished in biosensors for neurotransmitter evaluations in microfluidic lab-on-a-chip systems, smart wearable devices and other advanced technologies for the goal of real-time, portable diagnosis in neuroscience and clinics. These advances are paving the way for more effective applications of electrochemical sensing in neuroscience and clinical diagnostics.…”
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Research on the robustness of convolutional neural networks in image recognition
Published 2022-06-01“…Convolutional neural network is one of the key technologies in the application of image recognition and processing in artificial intelligence.Its wide application makes researches on its robustness more and more important.Previous researches on robustness of neural networks were too sweeping and most of them focused on adversarial robustness, which causes difficulty in further study in the mechanism of neural network robustness.The related researches of neuroscience were introduced and the concept of visual robustness was put forward.By studying the similarity and difference between neural network models and human visual system, the internal mechanism and faults of neural network robustness were revealed.The researches of neural network robustness in recent years were reviewed, and the reasons for the lack of robustness of neural network models were analyzed.The lack of robustness of neural networks is reflected in their sensitivity to small perturbations.The reason is that neural networks tend to learn high-frequency information for calculation and inference, which is difficult for humans to recognize.High-frequency information is easily affected by perturbations, and eventually causes mistakes of models.Previous researches on robustness mostly focused on mathematical properties of models, and were limited in the natural faults of neural networks.Visual robustness extends the traditional concept of robustness.The traditional concept of robustness measures the discrimination ability of models for distorted image examples.Distorted examples and clean examples can get correct outputs through robust models.Visual robustness measures the consistency between models and humans in discrimination ability.Visual robustness combines the research methods and achievements of neuroscience and psychology with artificial intelligence.The development of neuroscience in the field of vision were reviewed, and the application of research methods of cognitive psychology in neural network robustness were discussed.Human visual system has advantages in learning and abstract ability, whill neural network models have better performance in calculation speed and memory.The difference between the physiological structure of human brain and the logical structure of neural network models is the key factor leading to the problem of robustness of neural networks.The research of visual robustness requires deeper understanding of human visual system.Revealing the differences in cognitive mechanism between human visual system and neural network models and effectively improving the algorithm are the development trends of neural network robustness and even artificial intelligence.…”
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A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication
Published 2025-01-01“…Brain–computer interface (BCI) technologies for language decoding have emerged as a transformative bridge between neuroscience and artificial intelligence (AI), enabling direct neural–computational communication. …”
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Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel
Published 2025-01-01“…Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. …”
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Revealing and mitigating the inhibitory effect of serotonin on HRP-mediated protein labelling
Published 2024-12-01“…This technique has significantly advanced neuroscience by revealing sub-synaptic protein networks, such as the synaptic cleft and post-synaptic density. …”
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L’émersiologie
Published 2024-06-01“…This article explains how emersiology, born of from dialogue with Richard Shusterman, complements somaesthetics, notably through contributions from action neuroscience. It argues that the activity of the living body, felt involuntarily, crosses the threshold of consciousness to awaken oneself to information at the level of the lived body. …”
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