Articles tagged with Neural Networks
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A deep neural network developed by researchers at the University of California - Santa Cruz has been shown to accurately classify particle signals with 99.8% accuracy in real-time. The system can identify weak or noisy signals and pinpoint their source, making it suitable for point-of-care applications.
Researchers at Sainsbury Wellcome Centre found that instinctual exploratory runs enable mice to learn a map of the world efficiently. The study demonstrates how biological brains can learn faster and more efficiently than AI agents by focusing on salient objects.
A team of researchers developed a self-checking deep learning system that accurately extracts information from gravitational-wave data. The algorithm, called DINGO, has been trained to interpret real data and can cross-check its own results for accuracy.
A new model of the Earth's ionosphere has been developed using neural networks, which can reconstruct the topside ionosphere with high accuracy. This improvement is crucial for satellite navigation systems, such as global navigation satellite systems (GNSS), which require precise correction of radio signals to mitigate ionospheric delays.
Researchers developed a new neural network, MSUN, that accurately classifies plant diseases in natural settings using transfer learning and unsupervised domain adaptation. The model excelled in processing complex datasets and outperformed current crop of classifiers.
Researchers reconstructing neurons from ctenophore nerve net discover a continuous neural network, challenging the neuron doctrine. The finding has potential to provide key information on the evolutionary origin of nervous systems.
A recent study found that humans process social situations similarly, with an extensive neural network processing various social information. The study identified main dimensions of social perception, including antisocial behavior, sexual or affiliative behavior, and communication.
Researchers found that exercise releases chemical signals that promote neuronal development in the hippocampus, a crucial area for learning and memory. Astrocytes play a critical role in mediating the effects of exercise on brain health, helping to regulate neuronal activity and prevent hyperexcitability.
A team of UCF College of Medicine researchers created a digital topographical map of the cardiac sympathetic neural network, controlling heart rate and 'fight-or-flight' response. The map will guide treatments like neuromodulation therapy for hypertension, sleep apnea, and heart failure.
Researchers at UTHealth Houston identified two brain networks involved in reading, working together to integrate word meanings. The study used electroencephalography recordings from patients with epilepsy to measure neural activity while reading complex sentences.
Scientists have developed a system called PhAST, which uses light-emitting enzymes and ion channels to transmit information between neurons. This method has shown promising results in restoring communication in defective circuits and modifying animal behavior.
Researchers Pawel Burkhardt and Fred Wolf will study the simple nervous system of marine organisms to understand brain evolution. The project aims to reveal the neural network of ctenophores, a predator with an alien-like brain.
A new method using a back propagation neural network improves the accuracy of orbit prediction and position error covariance prediction for space targets. The method reduces prediction errors by up to 170m compared to existing models.
A team of researchers has developed a new human-in-the-loop system to improve the accuracy and interpretability of deep neural networks. The system uses an interactive one-click method for annotating images, reducing the co-occurrence bias inherent in training datasets.
MIT researchers discovered unconventional activation functions that enable optimal neural network performance, leading to better classification on various datasets. The findings suggest that selecting the correct activation function can significantly improve data accuracy in machine learning applications.
Researchers at USC have developed a new type of chip with the best memory of any chip thus far for edge AI. The chip uses metal oxide memristors to store information in a compact and stable way, eliminating the von Neumann bottleneck in current computing systems.
Researchers developed a deep learning approach to recognize and predict motion using vector-based relative change in position. The method, VecNet+LSTM, scored higher than other frameworks in recognizing motion and predicting future movements. This study has implications for machine learning in video analysis and artificial intelligence.
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.
Researchers developed a novel design for the chip using a crossbar layout, outperforming state-of-the-art photonic counterparts in terms of scalability and technical versatility. The synergy of powerful photonics with the novel crossbar architecture enables next generation neuromorphic computing engines.
A new approach to describing network connections can help predict system strong and weak points, crucial for understanding disease spread and communication networks. Researchers found that mapping hierarchies and incoherence within a system enables prediction of strong and weak connections.
A recent study has revealed two distinct types of cells in the locus coeruleus, a small nucleus in the brainstem, connected via gap junctions. This discovery has far-reaching implications for understanding neuropsychiatric and neurodegenerative conditions.
Researchers found that dogs with anxiety have altered brain connectivity, particularly between the amygdala and hippocampus. The study used fMRI to characterize abnormal neural networks in anxious dogs, providing insight into anxiety disorders in both animals and humans.
Researchers developed a deep learning-based AI model to automate cirrhosis identification using large amounts of data from EHRs. The model successfully identified patients with cirrhosis with a precision of 97%, offering potential for early diagnosis and improved management of the disease.
A cross-disciplinary team developed a convolutional neural network to analyze microscopy images of chromosomes with cohesion defects. The algorithm achieved 73.1% accuracy in classifying new images, streamlining experiments with chromosome analysis.
Researchers at Columbia University are developing algorithms that enable robots to understand object permanence and learn from 3D information. This allows robots to track objects and humans as they move around, improving their perception capabilities in indoor environments.
GPT-3 performs nearly on par with humans in decision-making but struggles with causal reasoning and information search. The language model's limitations may be due to its passive information-gathering approach, highlighting the need for active interaction with the world to achieve human-like intelligence.
Scientists at Max-Planck-Gesellschaft created an interactive atlas of gene expression in the zebrafish brain, revealing hundreds of genes with single-cell resolution. The new map integrates seamlessly with existing data, providing new insights into neural structure and function.
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.
Thanhvu Nguyen will receive $510,509 from the National Science Foundation to develop NeuralSAT, a constraint-solving framework for verifying deep neural networks. The project aims to improve DNN verification and scalability, tackling challenges in precision and soundness.
The study reveals that engram neurons form new synapses during fear memory formation, while memory extinction leads to the disappearance of these connections. The researchers observed this process using a novel dual-eGRASP system, enabling longitudinal observations of identical synapses at multiple time points.
Kyoto University researchers have created a map comparing circuit structure with neural activity in mammals, revealing a new mechanism behind visual cortex activities. This discovery sheds light on the hidden connections between neurons and could provide directions for constructing power-efficient deep neural networks.
Researchers at University of Gothenburg developed AI method using graph theory and neural networks to analyze cell movement, enabling better understanding of biological processes and development of new medical technologies. The method can reconstruct cell paths and test medication effectiveness as potential cancer treatments.
Researchers have developed a novel experimental platform that combines optical and fMRI techniques to study large-scale brain networks. The platform identified the anterior insular cortex as a key player in controlling the Default Mode Brain Network, which is active during daydreaming, memory retrieval, and envisioning the future.
Researchers at DZNE discovered that centrosome controls neuronal migration but not axon growth. The study used novel molecular tools to show that centrosomal activity influences radial migration of projection neurons.
Researchers at Cornell University developed an AI tool to track online debates and detect when tensions are escalating. ConvoWizard provides real-time feedback to users on potentially incendiary language, encouraging constructive debate.
A new study uses Fourier analysis to understand how deep neural networks learn complex physics. By analyzing the equation of a fully trained model, researchers were able to identify crucial information about how the network learns and generalizes. This breakthrough could accelerate the use of scientific deep learning in climate science.
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.
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 study of nearly 2 million Tweets in London and San Francisco identified specific events and locations associated with different emotions. The analysis showed that certain locations, such as hotels and restaurants, were linked to higher levels of joy, while train stations and transportation sites were linked to disgust.
Engineers at Tokyo Institute of Technology have developed a technique to support the classification performance of neural networks operating on sensor time series by feeding recorded signals into elementary non-linear dynamical systems. This approach increases the classification performance by augmenting the data through additional tim...
Researchers aim to create comprehensive maps of how neurons connect to each other, exploring neural circuits that underlie behavior. By studying connectivity and neural activity, scientists hope to understand the structure of the brain and its role in our sense of self.
A recent study published in Science Advances has deepened the understanding of circadian rhythm regulation in mammals. The research found that intracellular cyclic adenosine monophosphate (cAMP) is controlled by a neural network, whereas calcium ions are regulated by intracellular mechanisms.
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.
Prof. Shahar Kvatinsky's neuromorphic chip integrates storage and processing functionalities, achieving 97% handwritten letter recognition accuracy with low energy consumption. The chip's design enables potential applications in camera sensors, eliminating the need for digital image enhancement.
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 have developed a diffractive optical processor that can compute hundreds of transformations in parallel using wavelength multiplexing. The processor, which is powered by light instead of electricity, can execute multiple complex functions simultaneously at the speed of light.
Researchers discovered that ketamine increases background noise, impairing the function of thalamo-cortical neurons and affecting sensory perception. This finding may contribute to a better understanding of psychosis in schizophrenia.
Researchers at MIT's Picower Institute found that the brain stores information in working memory by making short-lived changes in neural connections, contradicting the traditional idea of sustained neuronal activity. This new insight sheds light on the sophisticated flexibility of thought and its dynamic nature.
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.
Researchers used auto-encoder technique to analyze 150 XRD patterns of magnetic alloys, identifying clusters and fine-tuning alloys by detecting relevant peaks. The approach enables accelerated development of high-efficiency materials with low environmental impact.
Neuroscientists have uncovered a brain circuit that enables mice to rapidly escape to shelter when faced with a threat. The retrosplenial cortex and superior colliculus form a circuit that encodes the direction to a shelter, allowing mice to accurately orient and escape.
Researchers developed an AI-based neural network to detect early knee osteoarthritis from x-ray images, matching doctors' diagnoses in 87% of cases. This method could help reduce unnecessary examinations, treatments, and even knee joint replacement surgery.
A USTC team has made significant advancements in atomistic neural network (AtNN) representations for chemical dynamics simulations. By decomposing system properties into atomic contributions, AtNN can satisfy different symmetries and periodicities, achieving accurate and efficient molecular dynamics simulations of complex systems.
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.
Researchers at Incheon National University have developed an IoT-enabled, real-time object detection system for autonomous vehicles. The YOLOv3-based model achieved high accuracy (>96%) in detecting 2D and 3D objects, outperforming other state-of-the-art detection models.
Researchers at Baylor College of Medicine discovered that oxytocin drives the development of neural connections in adult-born neurons. Oxytocin triggers a signaling pathway that promotes synapse maturation, enabling these new neurons to function properly.
Researchers have developed a text-to-audio model that can generate coherent and relevant music and sound from text, opening up new avenues for creative application and exploration. The model employed data compression methods to improve output quality and reduce training time.
Research reveals that brain function networks are affected differently by aging, gender, and blood immune factors. The study found correlations between cytokine clock, brain shrinkage, and gender, with females having a faster ticking cytokine clock.
A new study reveals ketamine dramatically changes neuronal activity patterns in the cerebral cortex, turning off active neurons and turning on silent ones. This switch in brain activity may impact our understanding of ketamine's antidepressant effects and future research in neuropsychiatry.
Researchers at Bar-Ilan University have created functional, multi-layered neural networks that mimic elements found in the brain of mammals. The 'mini-brains' were generated through magnetic manipulations of neural progenitor cells in a three-dimensional collagen substrate.