Articles tagged with Neural Modeling
A large-scale study found that diagnostic errors in neuroradiology were associated with longer interpretation times and higher shift volumes. The study also revealed a significant increase in diagnostic errors during weekend work, highlighting the need for targeted quality improvement interventions.
Researchers developed a VR imaging system to measure neural activity in mouse brains during behavior, revealing abnormalities in cortical functional network dynamics associated with autism. The system successfully distinguished between autism model mice and wild-type mice based on their brain network patterns.
A new technique combines machine learning with short-wave infrared fluorescence imaging to detect precise tumor boundaries with higher accuracy than traditional methods. The approach achieved a remarkable per-pixel classification accuracy of 97.5 percent and demonstrated robustness against changes in imaging conditions.
Researchers collected electrophysiological recordings from prefrontal cortical regions in three human subjects with severe treatment-resistant depression. They found lower depression severity correlated with decreased low-frequency neural activity and increased high-frequency activity.
Researchers from Dartmouth and University Medicine Essen found that strong links between brain measures and traits can be obtained when machine learning algorithms are utilized. This approach allows for high-powered results from moderate sample sizes, opening up studies of many traits and clinical conditions previously inaccessible.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
Researchers explore how AI language models like ChatGPT understand and respond to user input, mirroring their users' intelligence. The Reverse Turing Test reveals that chatbots reflect the intelligence level of their interviewers, incorporating their biases into responses.
A new AI model developed at the University of British Columbia accurately predicts cancer patient survival using natural language processing to analyze oncologist notes. The model shows over 80% accuracy in predicting six-month, 36-month and 60-month survival rates.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
A tiny worm called C. elegans is enabling scientists to explore the emerging theory that Parkinson’s disease starts in the gut, where a sticky protein causes neurons to die. The worms have a digestive tract and neurotransmitters similar to humans, making them a great model for studying cognitive problems associated with Parkinson's.
Researchers are developing a deep learning model to predict where waste is accumulating in cities and how often public bins should be cleared. This can help councils optimise their waste management services, schedule bin clearances and relocate rarely used bins.
Researchers at Gladstone Institutes found that ApoE4 from neurons plays a critical role in Alzheimer's disease, contrary to previous focus on glial cells. The study shows that deleting ApoE4 from neurons reduces brain changes resembling Alzheimer's disease, offering new possibilities for treatment.
A new study by NYU researchers found that infants excel at spotting motivations driving human actions, while state-of-the-art AI models struggle. By comparing infant and machine intelligence on a series of tasks, the researchers shed light on the limitations of current AI systems.
Researchers developed a testing model to understand cognitive challenges of superior semicircular canal dehiscence (SSCD), a rare condition causing sound-induced dizziness and hearing abnormalities. The Mongolian gerbil model shows promise for accelerating recovery with reversible diagnostic findings characteristic of patients with SSCD.
The new Cell Atlas updates reveal that 20% of all neurons in the mouse brain are inhibitory, with detailed information on morphological and electrophysiological properties. The atlas combines various datasets to create a coherent framework for simulating neural circuits.
Researchers at the University of Virginia Health System have identified a vital contributor to hyperactive immune responses and neuroinflammation in multiple sclerosis. Blocking this regulator alleviated harmful inflammation in lab mice, suggesting a potential therapeutic target for new treatments.
A new technique maps the effects of fire-induced permafrost thaw in Alaska, revealing widespread topographic change and vegetation shifts. The study used a machine learning-based approach to quantify thaw settlement across 3 million acres of land, with results showing a significant loss of evergreen forest and shrubland encroachment.
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
Researchers employed computer vision to extract social behaviors and linked them to brain synchronization patterns in a novel approach. During cooperative play, brain synchronization was strongest when participants shared gaze, while individual play showed increased within-brain synchronization.
A team of researchers developed a model-free approach using deep reinforcement learning to optimize estimation of multiple parameters in quantum sensors. The protocol achieved significantly better estimations compared to nonadaptive strategies, demonstrating enhanced performance in resource-limited regimes.
A UCF cancer researcher is studying the molecular causes of Alzheimer's disease by examining a protein deficiency in the brain. His research may lead to new targets for therapy and earlier diagnosis. The study focuses on KLF8, a protein important for brain function that has been recorded as deficient in patients with Alzheimer's.
Researchers developed a neural network model that uses terahertz time-domain spectroscopy data to predict burn healing outcomes with high accuracy. The new approach improves upon existing methods by reducing training data requirements, making it more practical for processing large clinical trials.
Researchers have developed a machine learning model that can predict the word about to be uttered by a subject based on their neural activity. The model achieved 55% accuracy using six channels of data and 70% accuracy using eight channels, comparable to other studies requiring electrodes over the entire cortical surface.
Researchers at the University of Essex have developed a new technique that uses non-invasive brain wave monitoring to accurately identify the music someone is listening to. The method has been successfully tested on a dataset of simple piano music and shows promise for decoding language signals from the brain in the future.
Researchers have discovered a unique, fast synapse in the inner ear that processes signals faster than any other in the human body. This breakthrough could lead to improved treatments for vertigo and balance disorders affecting millions of Americans over 40.
Researchers at Kyoto University found that a poor, low-yeast diet causes fruit flies' larvae to grow dendrites in an unexpected way. The hyperarborization phenotype is triggered by a simultaneous deficiency in vitamins, metal ions, and cholesterol, which increases the production of Wingless signaling molecules from body wall muscle.
A new study finds that autonomous vehicles could consume enough energy to generate significant greenhouse gas emissions, highlighting the need for rapid advancements in hardware efficiency. To mitigate this, researchers recommend more efficient autonomous vehicles with smaller carbon footprints.
Researchers found that dendritic cells in meninges produce mediators causing pain hypersensitivity after nerve injury. The kynurenine pathway, primarily driven by IDO1, is involved in pain production.
Scientists at Georgia Institute of Technology investigate how cats maintain balance while studying human spinal cord injury treatment.
A new AI model developed at USC accurately captures cognitive decline linked to neurodegenerative diseases like Alzheimer's much earlier than previous methods. The model analyzes MRI brain scans and detects subtle brain anatomy markers that correlate with cognitive decline, offering an unprecedented glimpse into human cognition.
The article discusses the use of Rapamycin in the context of Pascal's Wager, a philosophical framework used to justify beliefs. ChatGPT's generative pre-trained transformer model provides an exhaustive research perspective on the pros and cons of taking Rapamycin for anti-aging purposes.
A UCI-led study reveals that outbred Chilean degu rodents exhibit robust neurodegenerative features, including hippocampal neuronal loss and altered parvalbumin staining, resembling human Alzheimer's Disease. This provides a practical model for studying sporadic AD.
A review of over 100 scientific articles suggests that the safety of antidepressants during pregnancy is endorsed by science, but their effects on fetal neurodevelopment are poorly understood. Brazilian researchers propose using lab-grown mini-brains to investigate this impact and identify potential alterations.
A new report by The Hastings Center recommends clearer ethics guidelines and enhanced oversight for human-animal chimera research, which raises questions about animal welfare and moral status. The report also highlights the potential benefits of this research, including better disease models and organ transplantation.
Researchers at MIT and Harvard University have developed a new optogenetics-based tool to manipulate neuron excitability using light. By altering the electrical capacitance of cell membranes, they can change how strongly neurons respond to electrical input, with potential applications in learning, aging, and brain disorders.
Researchers discovered that krill oil protects dopaminergic neurons from age-related degeneration through temporal transcriptome rewiring and suppression of several hallmarks of aging. Krill oil increases neuronal resilience, promoting anti-oxidative stress and anti-inflammation, and abrogating multiple aging hallmarks.
A Rutgers-led study found that a gene mutation associated with autism causes an overstimulation of brain cells, disrupting the normal information flow. The researchers used human stem cells and transplanting them into mouse brains to understand how the mutation affects brain development.
Researchers create a new type of organic material that learns from its past behavior and emulates synaptic behaviour, enabling 'brain-like computing' networks. This breakthrough opens up opportunities in sustainable and green chemistry, energy, environment, and health.
A novel multi-modal image retrieval system, DenseBert4Ret, has been developed by researchers from Gwangju Institute of Science and Technology (GIST) using deep learning algorithms. The system outperforms state-of-the-art models in retrieving images based on both image and text features.
Researchers propose a new network approach to understand brain function, suggesting that connections between brain regions are key to cognitive tasks. This new view also sheds light on individual variability and can be applied to predict symptoms in patients with brain lesions.
Researchers trained a mouse brain model to solve visual tasks, achieving superior robustness and performance compared to traditional neural networks. The model's unique coding properties enable it to cope with errors and unexpected input, making it a promising tool for advances in neuromorphic computing.
Researchers at MIT have developed a machine-learning model that captures how sounds propagate through spaces, allowing for accurate visual renderings of rooms. This technique has potential applications in virtual and augmented reality, as well as improving AI agents' understanding of their environment.
A new computational model based on fruit fly brain data may help explain how humans process memories and experiences. The model suggests that living organisms, including humans, use a similar '1-2-3-many' count sketch to track encounters with familiar sights and smells.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Conscious perception of sound generates specific neuronal assemblies in the brain, differing from spontaneous brain activity. Under anesthesia, similar assemblies are present but lack sound-specificity, highlighting the importance of cortical creativity in sensory processing.
A team of Illinois Tech researchers used machine learning to estimate the age and gender of individual users with high accuracy, raising questions about data security and privacy. The study highlights the need for better regulations and best practices to protect personal information from being misused.
Researchers at the University of Oxford discovered that goldfish can accurately estimate distances by processing visual cues. The study found that goldfish use 'optic flow' to estimate distance, unlike terrestrial animals which rely on changes in angle between their eye and surrounding objects.
Researchers developed a new technique that enables on-device training using less than a quarter of a megabyte of memory, reducing the need for powerful computers and central servers. This approach preserves privacy by keeping data on the device, making deep learning more accessible for low-power edge devices.
Researchers from Xi'an Jiaotong-Liverpool University found that brain stimulation combined with a nose spray containing nanoparticles can improve recovery after ischemic stroke. The treatment increased cognitive and motor functions, and weighed more quickly than those treated with TMS alone.
A new research model allows scientists to study hereditary spastic paraplegias (HSPs) and test potential therapeutics. HSPs cause increased muscle tone in infants and young children, leading to wheelchair-bound individuals who lose motor function.
A new study proposes reducing toxic proteins in the blood as a potential therapy for Alzheimer's disease. By doing so, it aims to improve memory and learning capacity, as well as correct cognitive impairments. The next step is to test this treatment at a clinical level.
A new neurocomputational model introduces a three-level information processing framework to understand brain development and cognition. The model focuses on Hebbian learning and reinforcement learning, highlighting two fundamental mechanisms for multilevel cognitive ability development in biological neural networks.
A team of researchers from Japan successfully reconstructed common brain input signals from the firing rates of neurons using a method called superposed recurrence plot. This breakthrough has significant implications for artificial intelligence, neuroscience, and potential treatments for mental health disorders.
Researchers at Emory University used machine learning and fMRI to analyze a dog's brain activity while watching videos. The results show that dogs are more attuned to actions in their environment than to who or what is performing the action. This study offers proof of concept for decoding canine visual perception.
Researchers from Singapore University of Technology and Design (SUTD) have developed a new Brain-Inspired Replay model that enables continual learning in edge computing systems without storing data. This approach achieves state-of-the-art accuracy and high energy efficiency, overcoming the stability-plasticity issue in traditional models.
A study from MIT neuroscientists has identified a population of neurons in the visual cortex that respond to images of food. The researchers found four previously known populations and a fifth, more surprising population that appears to be selective for food images. This finding may reflect the special importance of food in human culture.
Researchers at Cedars-Sinai created complex computer models of individual brain cells using artificial intelligence. These models capture the electrical signals that neurons fire to communicate with each other, allowing researchers to replicate brain activity at the single-cell level.
Researchers found that the brain's cortex uses principles of calculus to implement a 'stop' signal, allowing for quick and precise decision-making in goal-directed behaviors. The study reveals how the brain integrates learned rules with sensory information to guide actions.
Researchers developed a novel convolutional neural network for facial expression recognition, outperforming conventional models while being computationally less expensive. The new model achieved an accuracy of 72.4% using only 58,000 parameters.
A new neurobiological model provides a unified framework for understanding various forms of creativity, including abstract thinking, improvisation, and divergent problem-solving. The model suggests that different brain areas are activated depending on the type of creativity, with dopamine playing a crucial role in controlling and optim...