Articles tagged with Neural Modeling
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.
Scientists at Max Planck Florida Institute for Neuroscience have identified a neural code that allows the brain to track distance traveled without visual cues. This ability is essential for navigation and memory formation, and may offer insight into early navigation problems in Alzheimer's disease.
Researchers propose that neural activity in each animal becomes linked through perception, allowing individuals to maintain bearings towards others relative to stable features in the environment. This synchronization gives rise to spontaneous alignment and collective movement.
A dual-source data-driven gated spatiotemporal fusion network significantly enhances the accuracy of fine-scale lightning forecasting. The model combines the strengths of weather foundation models and recent lightning observations, leveraging complementary features to improve forecast performance.
Professor John O'Brien's research focuses on understanding the impact of electrical synapses and gap junction plasticity on the retina and other neurological functions. The study aims to identify proteins that control electrical synapse strength, shedding light on their role in human disorders such as autism and seizures.
Researchers found that brain's dopamine neurons encode a map of possible future rewards across time and magnitude, guiding adaptive behavior in uncertain environments. This biological insight aligns with recent advances in AI, particularly distributional RL algorithms, which learn from reward distributions rather than averages.
Researchers have used AI and brain activity to study the Other-Race-Effect (ORE), a phenomenon where people recognize faces of their own race more easily than others. They found that faces from other races are processed more generally and with less detail, leading to less accurate recognition.
Researchers from the University of Texas at San Antonio discovered a significant link between REM sleep disturbances and increased PTSD severity. The study suggests that targeting REM sleep could be a promising approach for improving PTSD treatment outcomes.
A study published in Communications Psychology reveals how the brain forms a sense of agency when learning new motor skills. Researchers used a data glove to control a cursor on screen, finding that motor exploration plays a crucial role in developing self-agency.
The framework utilizes consortium blockchain architecture for immutable records, minimizing data falsification and cyberattacks. AI-driven tools ensure objective data collection and analysis, reducing human bias.
Researchers have developed a neuro-quantum leap in finding optimal solutions, leveraging Fowler-Nordheim annealers to discover new and unknown solutions. NeuroSA's structure is neuromorphic, with a search behavior determined by FN annealer, making it powerful for solving complex optimization problems.
A University of Ottawa-led study reveals that serotonin neurons are connected and interact with each other, controlling serotonin release in specific regions of the brain. This complex system has implications for understanding decision-making and developing targeted therapeutics for mood disorders.
A new study reveals promising progress toward predicting how patients with major depressive disorder will respond to antidepressant medications. Brain connectivity patterns were found to significantly improve predictions of treatment response across two large, independent clinical trials.
Researchers analyzed massive audio recordings to create a dictionary of short melodies in English-language prosody, assigning functions and meanings. They discovered hundreds of basic patterns with linguistic functions, including conveying attitudes such as curiosity or surprise, and identified syntactic rules governing their order.
Researchers at Texas Tech University Health Sciences Center have received a $1.94 million grant to study inhibitors that target peripheral neuropathic pain. The project aims to develop novel non-opioid and non-addicting therapies capable of effectively managing chronic pain.
A team of researchers developed Lp-Convolution, a novel method that uses multivariate p-generalized normal distribution to reshape CNN filters dynamically. This breakthrough improves the accuracy and efficiency of image recognition systems while reducing computational burden.
A study published in PLOS Biology found that individuals who receive dynamic facial-expression feedback from an avatar's face tend to gamble more than those receiving real human feedback. The amygdala plays a key role in this facilitation, with increased valuation of uncertainty contributing to increased risk-taking behavior.
Researchers investigated how brains process language during real-life conversations, employing AI to analyze neural activity and linguistic features. They found that speaking and listening engage widespread networks of brain areas in the frontal and temporal lobes, with highly specific patterns changing depending on words and context.
MIT researchers have found that a computational model of the ventral stream, which processes object recognition, also performs well on spatial tasks such as determining an object's location and orientation. This challenges the dominant perspective that the ventral stream is optimized for object recognition.
A novel channel-wise cumulative spike train image-driven model (cwCST-CNN) is presented for hand gesture recognition, achieving a classification accuracy of 96.92% in recognizing 10 gestures. The method leverages HD-sEMG signals and reconstructs them into two-dimensional images to capture spatial activation patterns.
A new hardware platform for AI accelerators capable of handling significant workloads with reduced energy requirement has been developed. The platform leverages III-V compound semiconductors to create photonic integrated circuits, which operate at the speed of light with minimal energy loss.
Scientists have built a digital twin of the mouse brain's visual cortex using AI, predicting neural activity and anatomical features. The model can generalize to new visual inputs and data, speeding up brain research and understanding intelligence.
Researchers investigated the neuroregenerative potential of 5-NOT and Epi, discovering they protected SH-SY5Y cells from MPP+ toxicity by regulating key proteins. The findings support 5-NOT as a glycomimetic drug candidate for Parkinson's disease treatment.
Researchers created a promising approach that augments LLMs with graph-based models to generate molecules with desired properties. The method generates molecules with better matching user specifications and valid synthesis plans, outperforming existing LLM-based approaches.
A University of Ottawa-led research team has deciphered the message that serotonin conveys to the brain, discovering a 'prospective code for value' that explains why neurons are activated by rewards and punishments. This finding has implications across multiple fields, including neuroscience, psychology, and psychiatry.
A new study published in Brain Medicine found that administering Osanetant shortly after a traumatic event significantly dampens fear expression in female mice. This suggests that targeting fear memory at its roots may be an effective intervention to reduce PTSD-like behaviors.
Researchers at Concordia University have developed a new approach to identifying fake news on social media using the SmoothDetector model. The model integrates probabilistic algorithms with deep neural networks to capture uncertainties and patterns in multimodal data, providing more nuanced judgments of authenticity.
Researchers developed a machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. The new surrogate model maintains the same level of accuracy as traditional particle-based simulations while reducing computation time from approximately 45 minutes to just three minutes.
A new study has revealed that Beagle dogs carrying mutations in the Shank3 gene exhibit face processing abnormalities, similar to those observed in human ASD patients. The research provides direct experimental evidence that mutations in Shank3 lead to ASD-like deficits in face processing, contributing to social impairments.
A new simulation of brain metabolism has revealed potential targets for future dementia treatments, according to a study published in Frontiers in Science. The model, available on the Open Brain Platform, could help scientists accelerate research into interventions promoting healthy brain aging.
Scientists at ISTA create a new brain organoid model that incorporates microglia to study viral infections, such as Rubella, and test the effectiveness of ibuprofen. The results show that microglia play a crucial role in inflammatory reactions and that ibuprofen exerts its protective effects by inhibiting two inflammatory enzymes.
Researchers from UC Berkeley and UCSF developed a streaming method that synthesizes brain signals into audible speech in near-real time, solving the latency challenge. The technology represents a critical step toward enabling communication for people who have lost the ability to speak, with high decoding accuracy and naturalistic fluency.
Researchers at Weill Cornell Medicine developed a new AI model that harnesses whole-slide tumor imaging data and gene expression analyses to predict how patients with muscle-invasive bladder cancer will respond to chemotherapy. The model outperforms previous models using a single data type, identifying key genes and tumor characteristi...
Researchers at HKUST established a pre-OPN OTI model to investigate functional axonal rewiring following CNS injury. They found that intrinsically photosensitive RGCs mediate functional recovery, and proposed a dual-intervention strategy to enhance regeneration efficiency
Researchers develop AI model to predict novel mutations in protein sequences, combining grammatical and semantic changes. The method uses all available information about the sequence and mutations to create a more accurate prediction model.
Researchers created a memristor-based brain-computer interface decoding system that achieves high accuracy while consuming significantly less energy than traditional systems. The system demonstrated impressive capabilities in real-world testing, including improved stability and accuracy through co-evolution with brain signals.
A new study presents a proof-of-concept leptomeningeal neural organoid (LMNO) fusion model to study meninges-brain signaling. The co-culture system of neural organoids fused with fetal leptomeninges from mice demonstrates stability and interface characteristics.
A new open-source model of brain metabolism has shown how altering key chemicals can restore aged cells to their youthful activity and resilience. The detailed simulation provides a glimpse into the impact of aging on brain metabolism and highlights potential drug targets.
The study systematically traces Generative AI evolution from deep learning to foundation models, highlighting four distinct stages and successful applications. Key challenges like safety concerns and theoretical breakthroughs require further attention and development in the field of Generative AI.
Crew-9 astronauts conducted research on protein crystallization studies and a new drug for Alzheimer's, dementia, and Parkinson's. They also supported student-led experiments, contributing to the next generation of explorers.
A new study by UCLA Health has discovered a drug that fully reproduces the effects of physical stroke rehabilitation in model mice. The drug, DDL-920, excites parvalbumin neurons, leading to significant recovery in movement control after stroke.
Researchers from Virginia Tech have published a visionary paper on fusing wireless technologies and AI to create human-like common sense. The team aims to develop a network that can think, plan, and imagine like humans, enabling seamless merging of physical, virtual, and digital dimensions.
Researchers have mapped the long-range synaptic connections involved in vocal learning in zebra finches, uncovering new details about how the brain organises learned vocalisations. The study provides a framework for understanding how the brain integrates sensory and motor information to guide learned vocal behaviour.
Researchers at Saarland University are developing leaner, customized AI models and techniques like knowledge distillation to reduce energy consumption. These smaller models enable small and medium-sized businesses to access powerful AI technology without a large technical infrastructure.
Researchers identified 11 cuproptosis-related genes and classified IDH1-mutant gliomas into two subtypes with distinct clinical outcomes. A risk model using FDX1/SLC31A1-based features demonstrated superior prognostic performance, providing insights for chemotherapy selection and treatment resistance.
Distributed acoustic sensing systems face data processing speed limitations; researchers leverage photonic neural networks to overcome these challenges. The TWM-PNNA system achieves high recognition accuracy above 90% with low power consumption, outperforming electrical GPUs by orders of magnitude.
Researchers at Baylor College of Medicine have identified new variants of CDKL2 and CDKL1 genes associated with developmental disorders, including epilepsy. The study proposes a mechanism by which defective variants cause neurological symptoms in affected individuals.
A new study challenges the long-held belief that fear is primarily communicated through facial expressions, suggesting instead that situational context plays a critical role in fear recognition. The research involved analyzing real-life fear reactions in videos and found that facial expressions alone fail to reliably signal fear.
A Virginia Tech study reveals that machine learning models are inadequate in detecting critical patient conditions, missing 66% of injuries. The research emphasizes the need for more responsive models incorporating medical knowledge to improve healthcare outcomes.
Researchers used advanced technology to analyze over 100 hours of brain activity during real-life discussions. The study found that the brain processes language in a sequence, from thinking about words to forming sounds and vice versa.
A new brain-mapping technique identified memory-related brain cells vulnerable to protein buildup, a key factor in Alzheimer's disease. The study found that certain cell types in the hippocampus and cortex were more affected by tau buildup.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.
IoTCIT 2025 invites researchers to submit papers on various areas of interest, including communication and Internet of Things. The conference aims to provide a platform for scholars to exchange research results and establish new ideas.
Researchers created a fiber computer that can be integrated into clothing to track health conditions and physical activity. The technology achieved an average accuracy of 70% when individually operated, but increased to nearly 95% when connected collectively.
This study utilized deep learning models to diagnose and predict the likelihood of malignant transformation in oral potentially malignant disorders. AI-driven approaches offer noninvasive, cost-effective, and objective means to enhance early detection and improve patient outcomes.
An international research team developed a user-friendly software method called Segment Anything for Microscopy, which can precisely segment images of tissues, cells, and similar structures. The new model improved performance for cell segmentation, enabling researchers to automate tasks that previously took weeks of manual effort.
A new AI model measures how fast the brain ages by analyzing MRI scans, providing a more accurate picture of brain health. The tool closely correlates faster brain aging with increased cognitive decline and dementia risk, offering potential for early biomarkers and personalized treatment.
New study finds that limitations on working memory exist due to learning, rather than capacity, and shed light on dopamine-related disorders like Parkinson's, ADHD, and schizophrenia. By understanding this process, clinicians may adopt different treatment options for patients with these conditions.
Researchers at WashU created a new model that uncovers brain mechanisms behind focusing amid distractions, finding people ignore past distractions but stay open to other inputs. The model demonstrates attention control by modulating focus on distractors and target tasks.
Research advances higher-order networks to capture multi-agent interactions, enabling accurate modeling of biological, social, and physical systems. The Dirac-Bianconi operator provides a powerful generalization of the graph Laplacian, encoding local and global interactions across different topological dimensions.
A new AI-based brain signal decoding model has improved how people with ALS use BCIs to predict their thoughts, achieving 74.06% accuracy in classifying left and right hand movement intention. The model's graph attention network design allows it to adapt to each user's unique brain patterns, leading to more consistent and personalized ...