Articles tagged with Neural Modeling
Researchers have developed a method to decode mouse neural activity, enabling accurate determination of location and direction within an open environment. This breakthrough could inform the design of intelligent machines that navigate autonomously without GPS or satellite guidance.
Scientists developed an innovative neural cell culture model that replicates the aberrant behavior of TDP-43 in neurons. The model identified NPTX2 as a toxic protein that accumulates in cells containing abnormal TDP-43, leading to neurodegeneration in ALS and FTD.
Scientists at Tokyo University of Science used deep learning to predict single-molecule magnets from a pool of 20,000 metal complexes, identifying 70% accuracy in distinguishing between SMMs and non-SMMs.
A recent study published in PLOS Biology found that the human brain can segregate direct speech from its echo, allowing for reliable recognition of echoic speech. This neural separation is essential for understanding conversations in noisy environments and is supported by magnetoencephalography recordings.
A new AI tool, DeepGO-SE, successfully predicts the molecular functions of unknown proteins with high accuracy. This breakthrough enables researchers to analyze uncharacterized proteins, facilitating tasks such as drug discovery, metabolic pathway analysis, and disease associations.
Researchers have identified a network of neurons controlling right-left movements in the brain, which may help treat Parkinson's disease. The discovery provides insight into how essential movements are produced by the brain.
ATH-1105, a small molecule positive modulator of the neurotrophic HGF system, demonstrates significant neuroprotective effects and extends survival in preclinical models of ALS. The study highlights the therapeutic potential of ATH-1105 in slowing or stopping neurodegeneration.
The team proposed a novel machine learning model with data augmentation, which accurately predicts the plastic anisotropic properties of wrought Mg alloys. The model showed significantly better robustness and generalizability than other models, paving the way for improved design and manufacturing of metal products.
Researchers trained an AI model on a single child's headcam video recordings from six months to two years, finding it could learn substantial words and concepts. The model linked words to visual counterparts, generalizing them to different instances, reflecting children's lab-based word learning.
A Brazilian study developed a method using AI to identify crop-livestock integration areas from satellite images. The approach can benefit agriculture by optimizing land use, diversifying farming activities, and encouraging sustainable practices.
Researchers have developed a novel optical neural network architecture that achieves nonlinear optical computation by precisely controlling ultrashort pulse propagation in multimode fibers. This approach streamlines the need for energy-intensive digital processes, achieving comparable accuracy with significantly reduced parameters.
Researchers have designed a new, affordable system to study neural interactions and compute using living neurons. The open-source MiV system boasts over 500 electrodes, offering improved control and precision in measuring neural processes.
A research group from Tohoku University Graduate School of Engineering has replicated human-like variable speed walking using a musculoskeletal model steered by a reflex control method reflective of the human nervous system. The breakthrough in biomechanics and robotics sets a new benchmark in understanding human movement.
A new study using generative AI models simulated how the brain learns and remembers events, revealing how memories are re-constructed in our minds. The model showed how the hippocampus and neocortex work together to create efficient 'conceptual' representations of scenes, enabling us to both recall past experiences and imagine new ones.
A new AI model has improved permafrost mapping by creating high-resolution maps of Arctic thawing, providing a tool for protecting infrastructure. The model achieved 83% accuracy in matching field data with its predictions, outperforming the widely used pan-arctic model.
Researchers propose a simple model that accurately describes neuronal connectivity in various organisms, suggesting that general networking principles govern brain organization. The model also provides an unexpected explanation for clustering phenomenon in social interactions and can be extended to other types of networks.
Researchers analyzed large datasets of neural wiring in fruit flies, mice, and worms to compare networks across species. They created a mathematical model based on Hebbian plasticity, which shows how strong connections form and leads to clustering, suggesting a shared principle of self-organization governs brain network formation.
A recent study by Tokyo University of Science has identified central emotions across languages through word association-based colexification networks. The researchers found that concepts like GOOD, WANT, BAD, and LOVE are associated with many other words representing emotions.
A new study led by Dr. Richard Naud of the University of Ottawa's Faculty of Medicine tackles the mystery of neuronal response variability, controlling output with dendrites' inputs to the core and little antennas
A new AI system developed by the University of Technology Sydney can rapidly detect COVID-19 from chest X-rays with high accuracy. The Custom Convolutional Neural Network (Custom-CNN) model streamlines the detection process, providing a faster and more accurate diagnosis.
A novel human brain organoid model generates all major cell types of the cerebellum, including functional Purkinje neurons. This breakthrough provides a new way to explore cerebellar development and disorders, advancing therapeutic interventions.
Researchers investigated the neural mechanisms of verbal working memory processing in healthy aging adults using magnetoencephalography. Age-related increases in theta activity were detected during encoding, and alpha and beta oscillations were stronger in older participants during maintenance and retrieval phases.
Researchers used deep learning models to identify compounds with strong antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA). The models were trained on expanded datasets and an algorithm that allows for explainable predictions, enabling the discovery of potent antibiotics with minimal human toxicity.
Scientists are finding that risk factors for autism spectrum disorder boil down to a couple of core pathways in early human brain development, according to researchers using technologies like RosetteArray. This new understanding is helping identify potential targets for treatments.
A recent study using AI to analyze registry data on people's residence, education, income, health, and working conditions can predict life events such as personality and time of death. The model outperforms other advanced neural networks and provides precise answers despite ethical concerns about sensitive data and bias.
Researchers discovered a striking similarity between AI models and the human brain's hippocampus, which enables powerful AI systems. By mimicking the NMDA receptor's gating process, they improved long-term memory in Transformer models.
A new study from MIT shows that computational models trained on auditory tasks display an internal organization similar to the human auditory cortex. Models trained on diverse tasks and background noise more closely mimic brain activation patterns.
Researchers at Salk Institute assembled the most complete atlas of the mouse brain by analyzing over 2 million brain cells. The detailed atlas reveals thousands of cell types, their connections, genes, and regulatory programs active in each cell, providing new insights into human disease vulnerabilities.
Researchers from the University of Technology Sydney have developed a portable, non-invasive system that can decode silent thoughts and turn them into text. The technology has been shown to achieve state-of-the-art performance in EEG translation, with an accuracy score of around 40% on BLEU-1.
Researchers use AI to develop dynamic modeling of brain graphs, capturing dynamics in continuous time for more accurate predictions and personalized treatment of brain diseases. The project aims to track disease development in individual patients and identify biomarkers associated with brain disorders.
Researchers from MIT and ETH Zurich developed a filtering technique to simplify a key intermediate step in MILP solvers, speeding up the process by 30-70% without compromising accuracy. A machine-learning model is then used to pick the best combination of algorithms for a specific optimization problem.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
The Cre-LoxP system's specificity is compromised due to non-specific promoters driving Cre expression, leading to inaccurate results. This limitation requires careful consideration for proper interpretation of experimental outcomes.
Researchers have developed new tools to assess disease progression of Alzheimer's disease in animal models, providing a translational approach for studying the disease. The tools use neuroimaging and network modeling techniques to analyze metabolic changes in the brain, confirming previous clinical findings.
Researchers used AI-selected natural images and synthetic images to probe visual processing areas of the brain, finding that predicted maximal activator images significantly activated targeted areas. The study suggests individualized models for each subject can improve understanding of visual system organization across populations.
Researchers found that 1,5-AF activated AMPK, leading to upregulation of the PGC-1α/BDNF pathway and alleviating aging-related decline in motor cognitive function. The study suggests that 1,5-AF can induce endogenous neurovascular protection, potentially preventing aging-associated brain diseases.
Researchers have identified a shared network of brain regions involved in various types of long-term memory, including general semantic, personal semantic, and episodic memory. The study found that these memory types rely on activating the same areas of the brain at differing magnitudes.
A new AI program created by researchers at UF and NVIDIA can generate medical records so well that human physicians couldn't tell the difference. The GatorTronGPT model uses a large language model to mimic natural human language, overcoming hurdles such as protecting patients' privacy and being highly technical.
Researchers developed a deep learning model that can identify previously unknown quasicrystalline phases in multiphase crystalline samples. The model achieved a prediction accuracy of over 92% and successfully detected an unknown phase in Al-Si-Ru alloys.
A new MIT study proposes a theoretical model that helps explain how cells maintain the memory of their cell type despite losing chemical modifications during DNA replication. The research team suggests that the 3D folding pattern of the genome determines which parts will be marked by these chemical modifications.
The Python code library snnTorch, developed by UC Santa Cruz's Jason Eshraghian, has surpassed 100,000 downloads and is used in various projects. A new paper published in the Proceedings of the IEEE documents the library and offers a candid educational resource for students and programmers interested in brain-inspired AI.
Researchers characterized changes in cognitive behaviors, neuronal morphology and gene expression in a tauopathy mouse model. The study found significant decreases in dendritic arborization and synaptic gene upregulation over time.
Researchers used AI models to analyze rat brain cells and found they can process high-level visual information like primates. This discovery sheds light on the visual system of rodents and has implications for understanding neurodegenerative disorders.
Jenny Wilkerson receives $1.94 million grant to develop novel models and investigate antinociceptive profiles of basal sex hormone alterations in patients with chronic post-op pain. The study aims to answer questions about the impact of altered sex hormone levels on recovery times, drug effectiveness, and long-term health.
Researchers developed a deep convolutional neural network to pinpoint cardiac catheter tip locations in photoacoustic images, achieving high precision and recall. The approach has the potential to replace fluoroscopy during cardiac interventions, leading to safer procedures.
A recent study published in the Proceedings of the National Academy of Sciences found that AI's deep convolutional neural networks can identify faces but struggle to capture other important information like emotional state and trustworthiness. Brain activity scans revealed a weak correlation between AI's codes and human brain represent...
The UTSA MATRIX AI Consortium has received a $2 million grant to create new AI models that rapidly learn, adapt, and operate in uncertain conditions. The team aims to bridge the gap between human brain processing efficiency and current AI limitations, enabling more efficient and adaptive AI systems.
A recent study found that transient inflammatory pain causes persistent mitochondrial and metabolic disturbances in sensory neurons, leading to failure in pain resolution. Targeting the cellular redox balance prevents and treats chronic inflammatory pain in rodents.
A research team at the University of Minnesota Medical School has identified the cerebellum as a critical component in coordinating brain networks essential for social recognition memory. This discovery holds promise for the development of targeted therapies for neurodegenerative disorders, which often feature loss of recognition memory.
Researchers are combining biology, physics, computer science, and engineering to design electric circuits that mimic the brain's adaptive behavior. The goal is to create a more efficient AI application that can learn from history and adapt without significant energy consumption.
A new hybrid method developed by Concordia researchers combines data from Weibull probability distribution and numerical weather prediction models to improve wind speed forecasting accuracy. This innovation has the potential to significantly enhance urban power generation, particularly in areas with high variability in wind speeds.
A team of scientists discovered two types of neurons in fruit flies and mice that enable them to identify distinct smells. With experience, these animals can learn to differentiate between very similar odors, a process that could improve machine-learning models and AI systems.
Researchers found self-supervised models generate activity patterns similar to mammalian brains, suggesting an organizing principle. The models learn representations of the physical world to make accurate predictions, potentially unlocking human-labeled data limitations.
A new method called TWC-Swin effectively restores holographic images even under low spatial coherence and arbitrary turbulence, surpassing traditional convolutional network-based methods. The study demonstrates strong generalization capabilities, extending its application to unseen scenes.
Researchers used computational models to analyze thousands of hours of transcribed audio recordings of children and adults interacting. The findings suggest that adults' ability to make context-based interpretations provides crucial feedback for babies acquiring language. These interpretations are critical for understanding what small ...
Researchers at Sainsbury Wellcome Centre find frontal and parietal cortex play key role in encoding value of economic choices when faced with uncertainty. The study provides foundation for understanding neurobiology of risky decisions.
Researchers have developed a new model that replicates key facets of Alzheimer's disease progression in the brain, enabling better understanding of how the disease develops and affects the brain. The model uses induced pluripotent stem cells to study human brain cells with the same genetic background as patients, revealing critical cha...
A recent study discovered two subgroups of people with idiopathic generalized epilepsy, one experiencing highest incidence during sleep and the other during daytime. The researchers found that either dynamics of cortisol or sleep stage transition explained most of the observed distributions of epileptiform discharges.
The 3rd annual Frontera User Meeting showcased the power of the NSF-funded supercomputer in various domains of science. Researchers presented findings on projects utilizing Frontera's capabilities, including compound storm surge models and nonlinear earthquake simulations. Additionally, scientists leveraged the system to analyze anonym...
Researchers found that deep neural networks often respond the same way to images with no resemblance to the target, generating unnatural signals. The models develop unique invariances that are different from human perceptual systems, causing them to perceive pairs of stimuli as similar despite their differences.