Articles tagged with Neural Networks
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
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Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
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Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Researchers used AI to train neural networks on complex behavioral tasks, revealing two distinct processes involved in short-term memory. These processes include a 'silent' process where the brain stores memories without ongoing neural activity, and a more active process where circuits of neurons fire continuously.
A recent study from MIT has found that dendrites are nearly always active when the main cell body of a neuron is active, suggesting a larger role in neural computation. The researchers used calcium imaging to measure activity in both soma and dendrites of individual neurons in the visual cortex.
A team of computer scientists has developed a method to analyze artistic portraiture, capturing facial features and individual style with high accuracy. The researchers used 'artistic augmentation' to transform photographic face data into more similar to artistic portraits.
Researchers developed a low-cost, sensor-packed glove that enables an AI system to recognize objects through touch alone. The glove produced high-resolution data at a fraction of the cost of existing sensors, allowing for accurate object classification and weight prediction with up to 76% accuracy.
Researchers studied inhibitory neurons' impact on brain oscillations using computer models. They found that these neurons can delay or facilitate the onset of synchronization, which is crucial for understanding brain diseases like Alzheimer's and epilepsy.
A study published in PNAS finds that premature infants who listened to tailored music had improved brain network development and functional connectivity compared to those without music. The research suggests that music can be a valuable tool to support the development of fragile newborns.
Research suggests both single neurons and large neural assemblies support spatial navigation; a potential link has been discovered between the two, with EEG oscillations potentially constituting the connection.
Researchers used zebrafish to study the effects of a genetic mutation linked to brain disorders such as autism and schizophrenia. The study found that the mutation caused clustering of cellular interactions, leading to disrupted normal development and brain health.
Researchers at North Carolina State University have developed AOGNets, a new framework for building deep neural networks via grammar-guided network generators. The new networks outperformed existing state-of-the-art frameworks in visual recognition tasks, achieving better prediction accuracy and model interpretability.
Researchers have developed a machine learning model to rapidly predict plasma behavior, allowing for real-time control of fusion reactions on Earth. The new model reduces calculation time from minutes to microseconds, enabling faster decision-making during experiments.
A team of researchers developed CosmoGAN, a deep learning network that generates high-fidelity convergence maps for weak gravitational lensing. The model achieves high statistical agreement with fully simulated maps, paving the way for building emulators out of deep neural networks.
Researchers studied embryonic development to understand how neurons regulate digestive movement. They discovered that intestinal nervous system coordinates muscular contractions and reflexes.
Researchers have developed a new framework that enables deep neural networks to learn new tasks while minimizing the loss of previously learned information. The Learn to Grow framework demonstrates improved performance in both new and old tasks, with backward transfer occurring when learning a new task enhances previous task accuracy.
A team of researchers from the Universities of Münster, Oxford, and Exeter have developed a light-based hardware that mimics the behavior of neurons and synapses in the brain. The chip can process data much faster than traditional computers, enabling applications such as medical diagnoses and cancer cell identification.
Scientists have developed a new method to quickly map brain connections, enabling systematic study of connection patterns within single individuals. The technique, combining infrared laser stimulation with functional MRI, reveals the direction of information flowing in the brain, critical for understanding brain processing.
Studies found that all brain parts process touch signals, complementing each other for perception. Brain network processing information as a single network with partially different functions from situation to situation.
Researchers have found that melanin-concentrating hormone neurons are active during rapid-eye movement (REM) sleep and when exploring novel objects in mice. This suggests these cells may facilitate memory formation through single-cell activity patterns.
A team of scientists at MIT developed a neural network that can read scientific papers and generate a plain-English summary. The system, called RUM, uses vectors rotating in multidimensional space to represent words and improve memory and recall capabilities.
A new neural network system developed by Stanford researchers enables autonomous cars to learn from past driving experiences and adapt to unknown conditions. The system performed similarly well as an experienced racecar driver in high-friction and low-friction scenarios, showing promise for improved safety.
Researchers developed a new 'multi-z' confocal microscopy system for imaging large groups of cells, enabling fast and detailed imaging across a wide field of view. The instrument captured cellular details at high speeds over a large 3D volume, providing unprecedented insights into how neurons interact during various behaviors.
Researchers at MIT have developed a neural architecture search algorithm that can directly learn specialized convolutional neural networks for target hardware platforms in only 200 GPU hours. The algorithm uses 'path-level' binarization and pruning to reduce memory consumption and improve efficiency.
Researchers at Harvard University have developed a novel brain implant that mimics the appearance, size, and flexibility of real neurons, allowing for stable monitoring of neural signals and potential treatment of neurological disorders. The implants inspire negligible immune response and may even encourage tissue regeneration.
Researchers developed a synchronization registration method with high sensitivity and selectivity, enabling the network to recognize up to 14 figures out of 102 possible variants. The system operates independently as a separate neural organism, utilizing multilevel neurons with high functionality.
Researchers at Karolinska Institutet have mapped brain networks that control the habenula, a structure linked to feelings of discomfort and aversion. The study suggests a specific pathway that can be modulated using optogenetics, offering hope for developing new treatments for depression and anxiety disorders.
A perception system for soft robots has been developed, mimicking human body components to predict complex motions and forces. The system uses a motion capture system, neural network, and soft sensors to interpret sensor signals, enabling accurate predictions of robot movements.
Researchers at Lobachevsky University have discovered the TrkB receptor system's role in forming neural networks. Activating this system increases complex functionally active neural networks with high transmission efficiency of nerve impulses, essential for learning and memory.
Researchers challenged a popular idea about how machine learning algorithms think by applying information theory to classification problems. They found that classifiers with many layers do not necessarily trade off between prediction and compression as previously thought.
Researchers at Max Planck Florida Institute for Neuroscience developed a new method to identify functional properties of individual synapses linking the two hemispheres. They found that callosal inputs and local inputs with similar orientation preference are clustered within the dendritic field, enabling coordinated network activity.
A new software, idtracker.ai, can identify up to 150 individual fish with high accuracy, extracting valuable data for understanding group behavior. The AI-powered system uses deep learning neural networks and conventional algorithms to recognize unique features of each zebrafish.
A new study led by Salk Institute researchers challenges the long-held view that individual brain cells operate as filters. The study found that the same neurons can prefer coarse or fine details depending on the context, and teaming up endows networks of neurons with flexibility to adapt to changing conditions.
Galanin-like peptide (GALP) plays a key role in regulating feeding behavior and energy metabolism. Recent research has shown that GALP administration leads to decreased respiratory quotient, indicating accelerated lipid metabolism.
Researchers have developed a neural network that can accurately detect age and gender from video frames, outperforming existing convolutional neural networks. The new method aggregates confidence levels using mathematical statistics and Dempster-Shafer theory, enabling the detection of age and gender with improved accuracy.
Researchers developed LEAP, a neural network-based system that tracks individual body parts in video footage, enabling the study of animal behavior and neural processes. The tool has great potential outside of neuroscience, automating labor-intensive analysis to study diverse animal locomotor behaviors.
A team of researchers developed a neuroinspired hardware-software co-design approach that can make neural network training more energy-efficient and faster. The approach uses a type of energy-efficient neural network called spiking neural networks, combined with the soft-pruning algorithm to minimize computing power and time.
The study combines synfire communication, coherence and resonance to provide insight into how messages are exchanged between brain areas. The researchers found that oscillations play a significant role in determining whether communication can take place.
Researchers found that isolating symptoms to a single brain area resulted in low reproducibility rates, but analyzing symptom-specific circuitry within brain networks led to 100% reproducibility. This new approach aims to shed light on therapy development by identifying common symptom-localization patterns across different diseases.
Researchers at MIT used a deep neural network to reconstruct transparent objects from low-exposure images taken in the dark. The technique could illuminate features of biological tissues and cells in low-light conditions, reducing the need for excessive light exposure that can damage specimens.
Researchers at Ruhr-University Bochum discovered that kainate receptors affect the development of brain cells immediately after birth, causing increased activity and dendrite growth. The discovery sheds light on the role of glutamate receptors in early maturation of nerve cells.
A novel framework combines CNNs and geometric inference methods to compute sketches and their corresponding 3D shapes faster and more intuitively than existing methods. Novice users can create stylish contents for their 3D creations using the tool.
Researchers develop novel method to realistically simulate dressing tasks using machine learning techniques, incorporating sense of touch to overcome challenges in cloth simulation. The approach enables single dressing sequences and a character controller that can successfully dress under various conditions.
Researchers at Carnegie Mellon University have developed a new method that uses neural networks to analyze single cell RNA sequencing data, identifying key genes and cell subtypes. This approach enables the analysis of all cell types, providing a method for comparative analysis.
Researchers have discovered three types of attacks that can be launched using a computer's graphics processing unit (GPU) to spy on web activity, steal passwords, and break into cloud-based applications. The attacks work by exploiting vulnerabilities in the GPU's memory utilization and performance counters.
A novel unsupervised language translation model developed by MIT researchers can translate far more languages with greater speed and efficiency. The model uses Gromov-Wasserstein distance to align words in two languages, achieving accurate results without human annotations.
Researchers used calcium imaging techniques to visualize spontaneous activity patterns in the mature visual cortex, finding precise organizational networks in the cerebral cortex much earlier than previously thought. These networks predict future brain function and are critical for processing complex sensory input.
Researchers discovered robust long-range patterns of correlated spontaneous activity in immature ferrets, contradicting expectations. These early activity patterns served as a template for the development of mature distributed networks, suggesting that 'local connections build a network activity scaffold'.
Scientists discovered that developing brain networks act locally to build globally, with spontaneous activity patterns correlating between distant populations of neurons. This finding suggests that long-range order originates from neural activity driven by short-range connections.
A neural network mimics the fruit fly's visual system and distinguishes between individuals based on sight alone. The system achieved an F1 score of 0.75, surpassing human biologists' performance in a similar task.
A deep neural network trained on annotated radiographs from senior orthopedic surgeons helps reduce misinterpretation of fractures in emergency department clinicians. The system demonstrated the transfer of expertise from specialists to generalist clinicians, leading to improved accuracy.
Researchers at the University of Alberta and Quantum Silicon Inc. have developed an atomic ultra-efficient electronics technology, enabling bespoke atomic patterns to control electrons. This innovation simulates neural networks, potentially training AI models more rapidly and accurately.
Researchers used deep convolutional neural networks to discriminate between signal and background tracks in the PandaX-III experiment, improving detection efficiency by 62% compared to traditional methods. The technique enhances our understanding of neutrinos and their role in matter-antimatter asymmetry.
Researchers use artificial neural networks to predict crystal stability in garnets and perovskites, achieving accuracy up to 10 times that of previous models. The team's web application allows for fast computation of material properties on various devices.
Researchers developed an artificial neural network that can compare medical concepts to specific symptoms mentioned by patients on social media. The AI system uses semantic vector word representation and can identify symptoms like insomnia or vertigo with high accuracy.
Researchers at UNIGE successfully resynchronized neurons to correct desynchronization in neural networks, suppressing behavioral symptoms associated with schizophrenia. The study, published in Nature Neuroscience, offers promising results for a new therapeutic approach targeting defective inhibitory neurons.
A team of scientists from Lehigh University has successfully engineered a living neural network that can perform basic learning tasks. The project, supported by the National Science Foundation, aims to develop new ways to think about computer design and may influence brain-related research.
Scientists at Argonne National Laboratory have developed a new method using neural networks to identify the structural signatures of molecular gases. This breakthrough enables researchers to accurately sense unidentified chemicals or scan samples for impurities in a much smaller period of time.
Scientists from TUM discovered that individual nerve cells create parallel connections to three areas of the brain, establishing feedback loops that reinforce salient stimuli while suppressing others. This automatic attention control mechanism is also shared by humans, revealing insights into perception and consciousness.
A new theory by Max Ortiz Catalan suggests that phantom limb pain results from neural entanglement between the missing limb's circuitry and pain perception networks. The 'stochastic entanglement' hypothesis explains how a novel treatment, Phantom Motor Execution (PME), can help alleviate pain by reactivating dormant brain areas.
Researchers at the University of Waterloo have developed a new AI-powered system that enables TVs to understand voice queries more accurately. The system, which was tested on Comcast's Xfinity X1 platform, can handle complex queries and personalize results based on user context.
A novel encryption technique combining homomorphic encryption and garbled circuits secures data used in online neural networks without significantly slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data.
Researchers use deep neural networks to recognize images transmitted over optical fibers, achieving high accuracy despite distortions caused by environmental factors. The technique has potential for improving endoscopic imaging in medical diagnosis and increasing the information-carrying capacity of fiber-optic telecommunication networks.