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.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
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.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
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 propose a new framework to quantify the predictability of temporal networks, which encodes the ordering and causality of interactions between nodes. The study found that the contributions of topology and temporality to network predictability vary significantly across different types of real networks.
Researchers at the University of Texas at Austin developed a method to make big data processing more energy efficient using magnetic components. By leveraging lateral inhibition in artificial neurons, they achieved an energy reduction of 20-30 times compared to standard back-propagation algorithms.
Researchers at MIT used machine learning to streamline the discovery process for new materials, narrowing down 3 million candidates to eight promising options in just five weeks. The neural network was able to predict properties and optimize criteria, improving upon conventional analytical methods.
Research in mice reveals that lactation temporarily changes how a mother's TIDA neurons regulate prolactin secretion, causing them to fire more frequently and out of rhythm. However, these changes are fully reversible after weaning, suggesting a unique adaptation to motherhood.
The Learning to Synthesize (LS-DNN) approach splits input signals into low and high spatial frequency bands, enabling deep neural networks to process and synthesize them. The algorithm is robust in handling noisy intensity signals, making it suitable for applications like x-rays and sonograms.
Researchers developed a computer program to identify each nerve cell in fluorescent microscope images of living worms, overcoming previous challenges by creating unique genetic modifications. The program uses a mathematical algorithm to analyze images and assign neuron identities based on position variations between individual animals.
Researchers developed a platform to coculture neurons and muscle cells, capturing the emergence of neuromuscular junctions and synchronized bursting patterns. The study provides new insights into biohybrid machines and their potential applications in fields like intelligent drug delivery and environment sensing.
A team from Deakin University in Australia developed an improved sight-correcting system for self-driving vehicles. By watching human operators complete tasks, the vehicles can learn to make decisions based on visual information, reducing the need for extensive training data.
A new chip has been developed at TU Wien that can recognize certain objects within nanoseconds, leveraging artificial intelligence and a special material. The chip integrates the neural network with its AI directly into the image sensor, making object recognition faster by many orders of magnitude.
Researchers successfully separate and observe single-molecule magnets (SMMs) on a magnetically neutral silica substrate using transmission electron microscopy. This breakthrough enables the development of auto-associative memories and multi-criterion optimization systems, mirroring the human brain.
Researchers found that brain cells generate a 'new song' with the same beat for each breath, adapting to changing rhythms throughout the day. The discovery could lead to new approaches to treating breathing disorders and may even help combat opioid-related deaths.
Researchers at MIT have developed a machine learning method to fill in the missing low-frequency seismic waves in human-generated seismic data, allowing for more accurate mapping of underground structures. The technique was trained on simulated earthquakes and used to infer missing frequencies from new input data.
Researchers at Mayo Clinic have created an artificial intelligence (AI) algorithm that can detect unseen characteristics of hypertrophic cardiomyopathy using standard EKGs. The AI's ability to diagnose the disease was found to be highly accurate, with an area under the curve of 0.96, outperforming traditional tests.
A multidisciplinary study led by UB researchers has developed a new experimental tool to study how neuronal networks recover their function after neuron loss. The study shows that the network quickly activates self-regulation mechanisms that reinforce existing connections and restore circuit functionality.
Researchers created a digital amplifier model using a deep neural network that can accurately simulate the sound of various guitar amplifiers, including popular brands like Marshall and Orange. The study uses black-box modelling to replicate the observed input-output mapping of analogue circuitry.
Researchers created a neural network that autonomously finds solutions well-adapted to quantum advantage demonstrations, aiding in developing new efficient quantum computers. This breakthrough enables the prediction of quantum advantages in complex networks, which is crucial for creating cost-effective and reliable quantum devices.
Scientists have identified recurring patterns in brain neurons that can be used to explain their behavior and function, paving the way for creating artificial intelligence that mimics the human brain. By understanding these patterns, researchers aim to develop new treatments for neurological disorders and improve current technology.
A deep learning method using a convolutional neural network (CNN) accurately differentiates between malignant and benign solid masses in small renal masses on contrast-enhanced CT scans. The corticomedullary phase showed the highest AUC value, indicating its effectiveness in malignancy prediction.
Researchers develop 'flash and freeze' method to study structure and function of synapses in intact neural circuits. The method allows for simultaneous observation of structural changes during signaling, revealing a near-identity between structurally and functionally defined vesicle pools.
Researchers developed a system to accurately detect space debris in Earth's orbit using laser ranging telescopes and neural networks. The new algorithm significantly improves the success rate of space debris detection, allowing for safer spacecraft maneuvers.
Scientists at Tokyo Institute of Technology found that overly strong connections can invert the effect of connectivity on complex activity, leading to more regular patterns. This phenomenon is observed in various natural and engineered systems, including neurons, coupled oscillators, and wireless terminals.
Researchers are testing whether changing brain's fuel source from glucose to ketones could potentially save neurons and neural networks over time. The study, funded by a $2.5 million grant, aims to understand how ketones affect brain cells and connectivity in the face of insulin resistance.
Researchers developed an innovative method to measure the complexity of image representations in deep neural networks, shedding light on their processing stages. The study found that classification accuracy depends on the network's ability to simplify information, with more accurate results from simplified representations.
A new method accelerates template creation for medical-image analysis, generating brain scan templates based on patient attributes such as age and sex. The model can synthesize atlases from sparse data, improving disease diagnosis accuracy.
Researchers from Russia and Germany discovered that activation of GDNF helps protect brain cells from death during hypoxic damage, maintaining neural network activity. This finding can lead to the development of an effective method for correcting CNS pathologies developing under oxygen deficiency.
Researchers at Argonne National Laboratory have developed domain-aware neural networks to replace expensive parameterizations in the Weather Research and Forecasting (WRF) model. These algorithms can predict environmental data more accurately with significantly less training data, enabling faster and higher-resolution simulations.
Researchers used deep neural networks to analyze ECG test results from over 2 million patients, identifying those at high risk of developing atrial fibrillation or dying within a year. The models were found to be superior in predicting mortality risk even in patients with normal ECGs.
Researchers developed a novel computational approach using deep artificial neural networks to predict neural responses to images. The study found that certain stimuli, such as checkerboards or sharp corners, elicit strong responses from neurons, contradicting current dogma in the field.
Researchers at Duke University have trained an AI tool to identify up to 200 species of birds from just a photo. The system, which uses deep learning, also shows its thinking by highlighting key patterns in the image.
Researchers developed an AI-based ozone forecasting system that can predict ozone levels with 85-90% accuracy. The model uses convolutional neural networks to analyze current conditions and forecast future ozone levels, improving health alerts for people at risk.
A new theory suggests that consciousness arises from synchronized neural activity and is guided by thermodynamic principles. During conscious states, the brain has higher entropy and more connected neural networks, leading to greater mental flexibility.
A team of researchers has successfully mapped the local connectome in the cerebral cortex using 3D electron microscopy, producing a connectome about 26 times larger than previous ones. The study provides insights into the density and magnitude of neuronal networks in the brain.
Researchers have developed an artificial intelligence technique that uses deep neural networks to analyze data from experiments on nanoscale ferroelectrics. This method has identified geometrically-driven differences in ferroelectric domain switching, providing new insights into the mechanisms of ferroelectric switching.
Researchers developed a neural network, PatchFCN, trained on 4,396 CT scans to detect brain hemorrhage abnormalities with accuracy similar to human experts. The algorithm achieved high accuracy and pixel-level delineation, classifying abnormalities into different pathological subtypes.
Researchers developed lipid-based memcapacitors that mimic biological synapses, accelerating routes to neuromorphic computing. The discovery could support the emergence of biology-inspired computing networks for sensory approaches to machine learning.
Researchers at the University of Delaware are developing new memory devices that can support neural networks in low-power embedded systems. These advancements aim to improve the lifetime and reliability of IoT devices, which currently struggle with battery power and memory constraints.
Researchers used repetitive transcranial magnetic stimulation to increase functional connectivity of a neural network implicated in memory. The study, published in eNeuro, confirms the effectiveness of this technique for experimental and clinical applications.
Researchers at Duke University use machine learning to model complex biological circuits, achieving speeds of hours instead of years or months. By training a deep neural network on large datasets, they uncover patterns and interactions between variables that were previously impossible to discover.
RUDN University mathematicians developed a model to optimize data center efficiency using Markov chains. Their method reduces server overheating and improves server capacity utilization, resulting in significant cost savings.
Duke University engineers used machine learning to design dielectric metamaterials that absorb and emit specific frequencies of terahertz radiation, reducing calculation time from over 2,000 years to just 23 hours. The new designs enable thermophotovoltaic devices that convert waste heat to electricity with higher efficiency.
Researchers have developed an electronic chip that can perform high-sensitivity intracellular recording from thousands of connected neurons simultaneously. This breakthrough has enabled the mapping of hundreds of synaptic connections and opens up new strategies for machine intelligence to build artificial neural networks.
A highly predictive genetic risk score is being developed by Paul Tran to identify children at significant risk of developing type 1 diabetes. The algorithm uses a feedforward neural network to analyze thousands of gene variants associated with the disease, aiming to predict with five times better accuracy than current systems.
Researchers have developed a brain-inspired, analog neural network that provides probabilistic responses for complex decision-making. The device is more energy efficient and produces less heat than current computing architectures.
Researchers have developed a new gene therapy that converts glial cells into neurons, improving motor function in mice and potentially treating stroke. The treatment uses the NeuroD1 gene and has been shown to increase neuronal density and reduce brain tissue loss in mouse models of stroke.
Researchers at Max Planck Florida Institute for Neuroscience developed a strategy to label and map local inhibitory inputs onto cells. They found that inhibitory inputs may parallel or diverge from target neurons, revealing a diverse palette of inhibition. This discovery suggests complex functional connectivity in the visual cortex.
Researchers use TDA to inject knowledge of real world into neural networks, reducing training time and increasing intelligibility. This approach enables machines to focus on meaningful features and improve performance in tasks like face recognition.
A study reveals that alternative splicing controls the identity and function of nerve cells, allowing for a complex neuronal network with limited genes. The research team mapped splice variants in different types of neurons, identifying unique repertoires that shape their characteristics.
Scientists have successfully grown miniature brains from stem cells that exhibit functional neural networks and produce brain waves resembling those of preterm babies. The study marks a significant breakthrough in understanding human brain development and may lead to new insights into diseases such as autism, epilepsy, and schizophrenia.
A two-layer all-optical artificial neural network has been successfully demonstrated for complex classification tasks, outperforming computer-based neural networks. The researchers plan to expand this approach to large-scale optical deep neural networks for specific practical applications.
Researchers from MIPT created a second-order memristor that stores information and forgets it over time, mimicking natural memory. The device is based on hafnium oxide and has potential applications in designing analog neurocomputers.
The UCLA researchers have significantly increased the system's accuracy by adding a second set of detectors to the system, representing each object type with two detectors rather than one. The new design takes advantage of parallelization and scalability of optical-based computational systems.
Researchers have developed an all-optical diffractive neural network that achieves unprecedented levels of inference accuracy, closing the performance gap with electronic neural networks. The design incorporates a differential detection scheme, which enables specialized sub-networks to recognize specific object classes.
Researchers at Purdue University have developed a new software called Emap2sec that can identify secondary structures in proteins from lower-resolution cryo-EM maps. This technique has the potential to speed up protein structure analysis and improve accuracy, enabling researchers to develop more effective drugs for various diseases.
A new computer system called EmoNet can accurately categorize images into emotional categories, suggesting that the visual cortex plays a crucial role in emotion processing. The study found that EmoNet could recognize emotions with high accuracy, even for nuanced emotions like confusion and awe.
Researchers from Forschungszentrum Jülich and RWTH Aachen University have identified a second critical mode in neuronal networks, allowing for parallel information processing. This newly discovered dynamics permits the network to represent signals in numerous combinations of activated neurons.
A new deep neural network architecture can identify manipulated images at the pixel level, detecting blurred boundaries and unnatural transitions between regions. This technology aims to improve photo editing tool security and detect deepfakes with high precision.
Scientists from Russia and Greece have successfully implemented a spiking neural network based on memristors, demonstrating the feasibility of local learning rules. The research enables autonomous unsupervised learning of complex neural networks, paving the way for new applications in AI.
Researchers from the University of Barcelona successfully synchronized two nanoscale optomechanical oscillators through mechanical coupling. The study demonstrates collective dynamics that can be controlled by acting externally on one oscillator only.
Researchers developed a new computational method using neural networks to simulate open quantum systems, predicting properties of large-scale quantum systems. This approach addresses the challenges of simulating intrinsically complex tasks with exponentially growing computational power.
Scientists have created functional neural networks derived from cerebral organoids, which can mimic the development of the human brain. The study provides a new tool for understanding brain function and may lead to breakthroughs in drug discovery, modeling neuropsychiatric disorders, and regenerative medicine.