Articles tagged with Neural Networks
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
Researchers built a two-stage warning system predicting solar flares within 48 hours via k-means clustering and neural networks. The model improved recall while increasing precision, but lost some positive sample information, affecting prediction accuracy.
A team of researchers developed a deep learning pipeline to analyze vascular system images of plants with high accuracy. The pipeline can detect vascular bundles, identify specific zones, and perform statistical analysis of traits in different stem internodes. This study has the potential to improve crop resilience and food security.
A proof-of-concept study developed three machine learning models to predict posttreatment recurrence in early-stage hepatocellular carcinoma patients. The models achieved high accuracy using imaging data alone, while combining clinical data did not significantly improve performance.
Despite DeepMind's neural network claiming superiority, scientists question its performance on predicting electron interactions in chemical systems. The BBB test set shows limited understanding of fractional-electron systems, raising concerns about the AI's ability to generalize.
Researchers developed a Flashover Prediction Neural Network (FlashNet) model to forecast deadly fire events, beating other AI-based tools with up to 92.1% accuracy across various building floorplans. The model's performance improved when given real-world data, highlighting its potential for saving firefighter lives.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.
Researchers at University of the Basque Country have developed a convolutional neural network to predict flow characteristics around flow control devices on wind turbines. The model achieves accurate results with minimal computational time, reducing errors compared to traditional CFD simulations.
Researchers at the University of Tokyo have made a groundbreaking discovery about the development of the visual system in mice. By studying the neural networks in cortical and thalamic regions, they found that parallel pathways from the retinas form earlier than connections within cortical areas, challenging current understanding of co...
Physicists have created a way to simulate quantum entanglement between interacting particles using neural networks and fictitious 'ghost' electrons. This approach enables accurate predictions of molecule behavior, which could lead to breakthroughs in pharmaceutical development and material design.
Researchers at MIT have developed a machine-learning system that uses computer vision to monitor the 3D printing process and correct errors in real-time. The system successfully printed objects more accurately than other 3D printing controllers, enabling engineers to incorporate novel materials into their prints with ease.
Researchers identified regions in the cerebral cortex and thalamus with high bidirectional connections, which are thought to be essential for consciousness. The findings support the idea that these networks are key to pinpointing the location of consciousness.
The new AI system uses associative learning to detect similarities in datasets, reducing processing time and computational cost. By leveraging optical parallel processing and light signals, the system can identify patterns and associations more efficiently than conventional machine learning algorithms.
Researchers propose a novel paradigm using nanoscale nonlinear fluid dynamics to support recurrent neural networks in neuromorphic computing. The liquid film functions as an optical memory, enabling 'reservoir computing' capable of performing digital and analog tasks.
Researchers at Max Planck Institute for Intelligent Systems created a robot dog named Morti that can walk smoothly within an hour. The robot uses a Bayesian optimization algorithm to learn from sensor data and adapts its virtual spinal cord, allowing it to optimize its walking pattern and minimize stumbling.
Researchers at NIST have developed a new type of hardware for AI that uses magnetic tunnel junctions, which are less energy-intensive than traditional silicon chips. The new technology has already passed a virtual wine-tasting test and shows promise for reducing energy use in AI systems.
Scientists at the Max Planck Institute have discovered a specialized neural circuit in zebrafish that enables recognition of conspecifics. This pathway, which runs from the retina to the thalamus, triggers shoaling behavior and regulates social approach and affiliation.
Researchers developed an AI system that classifies IBDN lesions accurately, displaying image-based diagnostic ability with 64.5% sensitivity and 89.5% specificity. The correct diagnosis rate of the AI system was 79.0, surpassing that of endoscopists, who achieved a 77.8% accuracy rate.
A Columbia University team created a robot that can learn and understand its own body, planning motion and avoiding obstacles without human assistance. The robot's self-model was accurate to about 1% of its workspace, paving the way for more self-reliant autonomous systems.
The ClearBuds earbuds use a novel microphone system and real-time machine-learning to enhance the speaker's voice and reduce background noise. They achieved better performance than Apple AirPods Pro in signal-to-distortion ratio tests.
Research finds that preschoolers' brain maturation improves inhibitory control abilities, with 4-year-olds outperforming 3-year-olds in tasks requiring stopping actions. The cognitive control network's distinct regions and white matter connections are associated with different aspects of self-control development.
A new theory developed by a collaboration between a former cosmologist and a computational neuroscientist has identified essential connections between brain cells. The theory, published in Physical Review Research, uses geometric framework to predict structure from function in neural networks.
Researchers have identified a neural circuit responsible for detecting 'affective' touch and influencing social behavior in mice. Activation of this circuit triggers social bonding, while disruption leads to reduced social interaction.
Researchers identify AgRP neurons as key players in regulating food intake by releasing endogenous lysophospholipids, which stimulate cerebral cortex activity. Administering autotaxin inhibitors can significantly reduce excessive food intake and obesity in animal models.
A new GPU-based machine learning algorithm, ReAL-LiFE, can rapidly analyze large amounts of data from diffusion Magnetic Resonance Imaging (dMRI) scans of the human brain. This allows for faster analysis and prediction of brain connectivity, enabling better understanding of brain-behaviour relationships at scale.
A multidisciplinary approach reveals a continuum of locomotion dynamics in lizards, with short-limbed species exhibiting snakelike waves and long-bodied species bending like lizards. The findings deepen understanding of evolution's implications for locomotion and have applications for advanced robotics designs.
Research reveals associations between cardiovascular risk factors, low physical fitness, and decreased neural activity in the social brain network, leading to social cognitive function decline. A healthy lifestyle may mitigate this decline through targeted interventions.
Researchers have discovered a prominent network of silencing interneurons in the human cortex, which could be linked to enhanced working memory and reasoning abilities. This unique network relies on abundant connections between inhibitory interneurons and is distinct from those found in mice.
Researchers found hyperactive amygdala-pons connectivity in individuals with major depressive disorder, associated with severity of psychological symptoms. The pons interacts with the amygdala to process sad affective information, suggesting a potential therapeutic target for mood regulation.
A team of researchers from Kyushu University has developed an olfactory sensor capable of identifying individuals by analyzing the compounds in their breath. The system, combined with machine learning, achieved an average accuracy of over 97% in authenticating up to 20 individuals.
A new AI system uses artificial neural networks to recognize objects more accurately and stably, despite changing visual inputs. The system mimics human eye movements to improve machine vision capabilities, reducing errors in self-driving cars and other applications.
A new computer model has been developed to rapidly scan cancer genomes and identify harmful driver mutations that contribute to tumor growth. The model, trained on genomic data from various types of cancer, found additional mutations in 5-10% of patients that could help doctors identify more effective treatment options.
Researchers at the Sainsbury Wellcome Centre discovered that brain area communication is dynamic and changes over rapid timespans, with influences varying on a fast timescale. This finding suggests that cortical areas may control different aspects of processing in downstream regions over very short time spans.
Research suggests that during deep sleep, neurons representing related items fire in close temporal order, triggering synaptic plasticity and forming strong connections. This process strengthens or creates new relational memories, which can be essential for learning connections between objects or people.
Researchers at the University of Rochester have created an automated scanning device that detects monolayers with high accuracy, reducing processing time and costs. The system utilizes AI-powered image processing to analyze images of materials, identifying monolayers with near 100% accuracy in just nine minutes.
Researchers have developed an imaging technique to capture information about brain tissue at the subcellular level, combining seven methods to visualize neural networks and individual cells. This approach allows for a complete picture of brain structure and function, overcoming challenges of imaging tissues at different scales.
Researchers propose a new method to study neural networks using intrinsic signal optical imaging (ISOI), which provides detailed maps of brain activity in living subjects. The study shows that ISOI can reveal cortical architecture at columnar resolution, offering a more accurate picture of brain network activity than existing methods.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
A new tool called DeepSqueak uses deep learning to identify marine mammal calls with high accuracy, even in noisy environments. The tool was originally developed for rodent ultrasound signals but has been adapted to detect sounds at other frequencies, including humpback whales and delphinids.
Researchers have developed a new training method for machine learning models to perform blood cell counts, reducing manual annotation work. The U-Net model achieves high accuracy in segmenting images with multiple cell types, promising a simpler and cheaper alternative to traditional cell analyzers.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
Researchers at Duke University have developed a machine learning algorithm that incorporates known physics into neural networks, allowing for new insights into material properties and more efficient predictions. The approach helps the algorithm attain transparency and accuracy, even with limited training data.
A research team developed a model that mimics human judgment to distinguish between reflective and transparent materials. The model outperformed humans in accuracy but struggled to identify image clues.
Researchers at EPFL's School of Life Sciences create a digital twin of Drosophila called NeuroMechFly, which uses biomechanical modeling and machine learning to simulate the fly's movements. The model is validated through experiments that demonstrate its accuracy in replicating real animal behaviors.
A new study found that acute stress and repeated traumas in adolescents alter functional connectivity between the default mode, salience, and central executive networks. This may lead to a maladaptive response to stressful experiences and increased neural vulnerability.
A new study suggests that supplementing a diet with Ascidiacea, also known as sea squirts, reverses some main signs of aging in animal models. The researchers found that plasmalogens, vital to body processes, decrease with age and contribute to neurodegenerative diseases like Alzheimer's and Parkinson's.
A team of scientists has discovered a bi-directional neural network connecting the legs and visual system in fruit flies, enabling them to walk while tracking their steps. The study reveals that this network supports walking on two different timescales simultaneously.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
Researchers at Caltech developed Neural-Fly, a deep-learning method that enables drones to adapt to wind conditions in real-time. The method achieved significant improvements in drone performance compared to existing adaptive control algorithms.
Researchers mapped neural networks in marmoset and macaque monkeys to find that multiple areas in the frontal lobe control vocalization, contradicting a long-held view. This discovery could lead to a better understanding of speech disorders such as stuttering and apraxia.
A new case study reveals significant differences between human and AI perception in breast-cancer screenings. Researchers found that AI systems consider tiny details in mammograms that are irrelevant to radiologists, highlighting the need for understanding and correcting AI decision-making before trusting it for life-critical medical d...
Researchers developed a machine-learning method that allows robots to pick up and place never-before-seen objects in random poses, requiring only 10 human demonstrations. The system uses a neural network specifically designed to reconstruct 3D shapes, enabling the robot to generalize to new object orientations.
Researchers at Salk Institute discover that brain parses information through interactions of waves of neural activity, changing how data is processed and affecting attention and focus.
A new deep learning method, PDD-Net, uses 3D deep convolutional neural networks to extract deep metabolic imaging indices from PET scans for differential diagnosis of parkinsonian diseases. The method achieved high sensitivity and specificity rates for Parkinson's disease and other parkinsonian syndromes.
Researchers developed an AI algorithm to model first impressions and accurately predict how people will be perceived based on a photograph of their face. The algorithm's findings align with common intuitions or cultural assumptions, such as people who smile being seen as more trustworthy.
A study at the University of Helsinki found that in utero exposure to mother's antiepileptic or antidepressant medication can lead to widespread changes in cortical networks, affecting local and global brain function. This may have implications for infants' neuropsychological development and future research on environmental factors.
A new control allocation method using a neural network improves the performance of quadrotor controllers by considering aerodynamic effects. This approach reduces errors in command generation and delivers better thrust and torque signals.
A new study found that microglia regulate neuronal subtypes differently in response to bacteria, affecting intrinsic excitability. Pyramidal cells exhibited lower excitability, while Purkinje cells showed higher excitability when modulated by microglia.
A study by Tokyo University of Science researchers has demonstrated that a computationally-light model can simulate complex brain cell responses, including periodic and quasi-periodic responses. The Izhikevich neuron model was found to be capable of reproducing both types of responses at lower computational cost.
A project aims to develop software toolkits to assess neural network robustness and potential security vulnerabilities. The goal is to create a framework for building secure AI systems, emphasizing human expertise in data collection and testing.