Articles tagged with Neural Networks
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.
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 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.
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.
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.
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.
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 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.
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.
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 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 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.
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.
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.
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 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.
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 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.
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.
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.
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 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.
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.
The UNC Charlotte team developed a universal AI algorithm called AutoClass to clean noisy single-cell RNA sequencing (scRNA-Seq) data. The algorithm effectively removes noise and enhances downstream analysis in multiple aspects, demonstrating its robustness and scalability.
Researchers have identified the complete series of 10 factors that regulate the development of brain cell types in the visual system of fruit flies. This discovery opens new avenues of research to understand how brain development evolved in different animals and holds clues for regenerative medicine.
A novel 'rational' neural network reveals underlying mathematical equations through Green's functions, enabling humans to understand machine-generated findings. This breakthrough in partial differential equation learning holds promise for advancing scientific exploration of weather systems, climate change, and more.
A new study suggests that a high-salt diet can lead to the hyperactivity of brain cells, resulting in increased constriction of blood vessels and worsening of cardiometabolic diseases. The research also found that excessive salt consumption can trigger an unusual response in which neurons become more active despite reduced blood flow.
Researchers from the University of Cambridge and Oslo identify a century-old mathematical paradox as the Achilles' heel of modern AI. The paradox limits the existence of stable and accurate neural networks, making many AI systems untrustworthy in high-risk areas.
Researchers reexamined hundreds of experiments on neural activity and consciousness, finding that experiment parameters determine results. The study used artificial intelligence to predict which theory would be supported by each experiment with 80% success.
Researchers at Duke University found a collection of coordinated brain regions that predict and direct social behavior in mice. By analyzing the electrical activity of these regions, they identified how social or solitary an individual mouse is and were able to prompt them to be more gregarious. This study may lead to better diagnostic...
A study published in Frontiers in Microbiology has found that machine learning analysis of microscopy images can be used to identify bacteria resistant to antibiotics. Researchers discovered that shape changes in bacterial cells can predict drug resistance, suggesting a new approach for detecting and predicting drug resistance.
Researchers at Kaunas University of Technology improved an algorithm to detect Alzheimer's disease from MRI images, achieving over 98% accuracy. The new model uses a modified neural network and adapts to variations in data, such as differences in hospital equipment and patient positions.
A study found that intact astrocyte networks are essential for neural homeostasis, synaptic plasticity, and spatial cognitive abilities in adult mice. Disrupting these networks impairs spatial learning and memory due to altered neuronal excitability and compromised synaptic transmission.
A new MRI probe can monitor individual populations of neurons and reveal how they interact with each other. The technique uses genetically targeted probes to detect neural activity and provide a more precise picture of brain function.
A team of scientists developed a soft haptic sensor that can accurately estimate contact points and forces using computer vision and deep neural networks. The sensor is sensitive enough to detect even tiny forces and detailed object shapes.
A deep learning model has been developed to infer horizontal motion on the Sun's surface using temperature and vertical motion data. The technique shows promise for future high-resolution solar observations and laboratory plasmas.
KAUST researchers develop an artificial electronic retina that mimics human vision and recognizes handwritten numbers with high accuracy. The retina uses perovskite nanocrystals to detect light intensity via capacitive change, offering a more energy-efficient alternative to existing systems.
Research reveals that the brain's electrical rhythms fluctuate between high precision and low precision states several times per second, affecting how relevant information is transmitted. Cross-frequency coupling enables selective attention by modulating the strength of different frequencies, while distinguishing between different type...
The University of Essex team has devised a new approach to training neural networks called Target Space, which stabilizes the learning process by tweaking neuron firing strengths. This method enables deeper neural networks with fewer training examples and computing resources, accelerating AI breakthroughs.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
Researchers at Tokyo Institute of Technology have developed a new AI processor called Hiddenite, which achieves state-of-the-art accuracy in sparse neural networks with lower computational burdens. The chip drastically reduces external memory access for enhanced computational efficiency.
A team of researchers from Skoltech and universities developed a neural network-based solution for automated recognition of chemical formulas on research paper scans. The algorithm combines molecules, functional groups, fonts, styles, and printing defects to mimic existing molecular template depiction styles.
A new machine learning process has been developed to identify and classify hip fractures from X-rays, achieving 92% accuracy and confidence. This approach aims to improve patient outcomes, reduce care costs, and alleviate the bottleneck of unreported radiographs in the UK.