Articles tagged with Neural Networks
Researchers used repetitive transcranial magnetic stimulation to increase functional connectivity of a neural network implicated in memory. The study, published in eNeuro, confirms the effectiveness of this technique for experimental and clinical applications.
Researchers at Duke University use machine learning to model complex biological circuits, achieving speeds of hours instead of years or months. By training a deep neural network on large datasets, they uncover patterns and interactions between variables that were previously impossible to discover.
RUDN University mathematicians developed a model to optimize data center efficiency using Markov chains. Their method reduces server overheating and improves server capacity utilization, resulting in significant cost savings.
Duke University engineers used machine learning to design dielectric metamaterials that absorb and emit specific frequencies of terahertz radiation, reducing calculation time from over 2,000 years to just 23 hours. The new designs enable thermophotovoltaic devices that convert waste heat to electricity with higher efficiency.
Researchers have developed an electronic chip that can perform high-sensitivity intracellular recording from thousands of connected neurons simultaneously. This breakthrough has enabled the mapping of hundreds of synaptic connections and opens up new strategies for machine intelligence to build artificial neural networks.
A highly predictive genetic risk score is being developed by Paul Tran to identify children at significant risk of developing type 1 diabetes. The algorithm uses a feedforward neural network to analyze thousands of gene variants associated with the disease, aiming to predict with five times better accuracy than current systems.
Researchers have developed a brain-inspired, analog neural network that provides probabilistic responses for complex decision-making. The device is more energy efficient and produces less heat than current computing architectures.
Researchers have developed a new gene therapy that converts glial cells into neurons, improving motor function in mice and potentially treating stroke. The treatment uses the NeuroD1 gene and has been shown to increase neuronal density and reduce brain tissue loss in mouse models of stroke.
Researchers at Max Planck Florida Institute for Neuroscience developed a strategy to label and map local inhibitory inputs onto cells. They found that inhibitory inputs may parallel or diverge from target neurons, revealing a diverse palette of inhibition. This discovery suggests complex functional connectivity in the visual cortex.
A study reveals that alternative splicing controls the identity and function of nerve cells, allowing for a complex neuronal network with limited genes. The research team mapped splice variants in different types of neurons, identifying unique repertoires that shape their characteristics.
Researchers use TDA to inject knowledge of real world into neural networks, reducing training time and increasing intelligibility. This approach enables machines to focus on meaningful features and improve performance in tasks like face recognition.
A two-layer all-optical artificial neural network has been successfully demonstrated for complex classification tasks, outperforming computer-based neural networks. The researchers plan to expand this approach to large-scale optical deep neural networks for specific practical applications.
Scientists have successfully grown miniature brains from stem cells that exhibit functional neural networks and produce brain waves resembling those of preterm babies. The study marks a significant breakthrough in understanding human brain development and may lead to new insights into diseases such as autism, epilepsy, and schizophrenia.
Researchers from MIPT created a second-order memristor that stores information and forgets it over time, mimicking natural memory. The device is based on hafnium oxide and has potential applications in designing analog neurocomputers.
The UCLA researchers have significantly increased the system's accuracy by adding a second set of detectors to the system, representing each object type with two detectors rather than one. The new design takes advantage of parallelization and scalability of optical-based computational systems.
Researchers have developed an all-optical diffractive neural network that achieves unprecedented levels of inference accuracy, closing the performance gap with electronic neural networks. The design incorporates a differential detection scheme, which enables specialized sub-networks to recognize specific object classes.
Researchers at Purdue University have developed a new software called Emap2sec that can identify secondary structures in proteins from lower-resolution cryo-EM maps. This technique has the potential to speed up protein structure analysis and improve accuracy, enabling researchers to develop more effective drugs for various diseases.
A new computer system called EmoNet can accurately categorize images into emotional categories, suggesting that the visual cortex plays a crucial role in emotion processing. The study found that EmoNet could recognize emotions with high accuracy, even for nuanced emotions like confusion and awe.
Researchers from Forschungszentrum Jülich and RWTH Aachen University have identified a second critical mode in neuronal networks, allowing for parallel information processing. This newly discovered dynamics permits the network to represent signals in numerous combinations of activated neurons.
A new deep neural network architecture can identify manipulated images at the pixel level, detecting blurred boundaries and unnatural transitions between regions. This technology aims to improve photo editing tool security and detect deepfakes with high precision.
Scientists from Russia and Greece have successfully implemented a spiking neural network based on memristors, demonstrating the feasibility of local learning rules. The research enables autonomous unsupervised learning of complex neural networks, paving the way for new applications in AI.
Researchers from the University of Barcelona successfully synchronized two nanoscale optomechanical oscillators through mechanical coupling. The study demonstrates collective dynamics that can be controlled by acting externally on one oscillator only.
Researchers developed a new computational method using neural networks to simulate open quantum systems, predicting properties of large-scale quantum systems. This approach addresses the challenges of simulating intrinsically complex tasks with exponentially growing computational power.
Scientists have created functional neural networks derived from cerebral organoids, which can mimic the development of the human brain. The study provides a new tool for understanding brain function and may lead to breakthroughs in drug discovery, modeling neuropsychiatric disorders, and regenerative medicine.
Researchers used AI to train neural networks on complex behavioral tasks, revealing two distinct processes involved in short-term memory. These processes include a 'silent' process where the brain stores memories without ongoing neural activity, and a more active process where circuits of neurons fire continuously.
A recent study from MIT has found that dendrites are nearly always active when the main cell body of a neuron is active, suggesting a larger role in neural computation. The researchers used calcium imaging to measure activity in both soma and dendrites of individual neurons in the visual cortex.
A team of computer scientists has developed a method to analyze artistic portraiture, capturing facial features and individual style with high accuracy. The researchers used 'artistic augmentation' to transform photographic face data into more similar to artistic portraits.
Researchers developed a low-cost, sensor-packed glove that enables an AI system to recognize objects through touch alone. The glove produced high-resolution data at a fraction of the cost of existing sensors, allowing for accurate object classification and weight prediction with up to 76% accuracy.
Researchers studied inhibitory neurons' impact on brain oscillations using computer models. They found that these neurons can delay or facilitate the onset of synchronization, which is crucial for understanding brain diseases like Alzheimer's and epilepsy.
A study published in PNAS finds that premature infants who listened to tailored music had improved brain network development and functional connectivity compared to those without music. The research suggests that music can be a valuable tool to support the development of fragile newborns.
Research suggests both single neurons and large neural assemblies support spatial navigation; a potential link has been discovered between the two, with EEG oscillations potentially constituting the connection.
Researchers used zebrafish to study the effects of a genetic mutation linked to brain disorders such as autism and schizophrenia. The study found that the mutation caused clustering of cellular interactions, leading to disrupted normal development and brain health.
Researchers at North Carolina State University have developed AOGNets, a new framework for building deep neural networks via grammar-guided network generators. The new networks outperformed existing state-of-the-art frameworks in visual recognition tasks, achieving better prediction accuracy and model interpretability.
Researchers have developed a machine learning model to rapidly predict plasma behavior, allowing for real-time control of fusion reactions on Earth. The new model reduces calculation time from minutes to microseconds, enabling faster decision-making during experiments.
A team of researchers developed CosmoGAN, a deep learning network that generates high-fidelity convergence maps for weak gravitational lensing. The model achieves high statistical agreement with fully simulated maps, paving the way for building emulators out of deep neural networks.
Researchers studied embryonic development to understand how neurons regulate digestive movement. They discovered that intestinal nervous system coordinates muscular contractions and reflexes.
Researchers have developed a new framework that enables deep neural networks to learn new tasks while minimizing the loss of previously learned information. The Learn to Grow framework demonstrates improved performance in both new and old tasks, with backward transfer occurring when learning a new task enhances previous task accuracy.
A team of researchers from the Universities of Münster, Oxford, and Exeter have developed a light-based hardware that mimics the behavior of neurons and synapses in the brain. The chip can process data much faster than traditional computers, enabling applications such as medical diagnoses and cancer cell identification.
Scientists have developed a new method to quickly map brain connections, enabling systematic study of connection patterns within single individuals. The technique, combining infrared laser stimulation with functional MRI, reveals the direction of information flowing in the brain, critical for understanding brain processing.
Studies found that all brain parts process touch signals, complementing each other for perception. Brain network processing information as a single network with partially different functions from situation to situation.
Researchers have found that melanin-concentrating hormone neurons are active during rapid-eye movement (REM) sleep and when exploring novel objects in mice. This suggests these cells may facilitate memory formation through single-cell activity patterns.
A team of scientists at MIT developed a neural network that can read scientific papers and generate a plain-English summary. The system, called RUM, uses vectors rotating in multidimensional space to represent words and improve memory and recall capabilities.
A new neural network system developed by Stanford researchers enables autonomous cars to learn from past driving experiences and adapt to unknown conditions. The system performed similarly well as an experienced racecar driver in high-friction and low-friction scenarios, showing promise for improved safety.
Researchers developed a new 'multi-z' confocal microscopy system for imaging large groups of cells, enabling fast and detailed imaging across a wide field of view. The instrument captured cellular details at high speeds over a large 3D volume, providing unprecedented insights into how neurons interact during various behaviors.
Researchers at MIT have developed a neural architecture search algorithm that can directly learn specialized convolutional neural networks for target hardware platforms in only 200 GPU hours. The algorithm uses 'path-level' binarization and pruning to reduce memory consumption and improve efficiency.
Researchers at Harvard University have developed a novel brain implant that mimics the appearance, size, and flexibility of real neurons, allowing for stable monitoring of neural signals and potential treatment of neurological disorders. The implants inspire negligible immune response and may even encourage tissue regeneration.
Researchers developed a synchronization registration method with high sensitivity and selectivity, enabling the network to recognize up to 14 figures out of 102 possible variants. The system operates independently as a separate neural organism, utilizing multilevel neurons with high functionality.
Researchers at Karolinska Institutet have mapped brain networks that control the habenula, a structure linked to feelings of discomfort and aversion. The study suggests a specific pathway that can be modulated using optogenetics, offering hope for developing new treatments for depression and anxiety disorders.
A perception system for soft robots has been developed, mimicking human body components to predict complex motions and forces. The system uses a motion capture system, neural network, and soft sensors to interpret sensor signals, enabling accurate predictions of robot movements.
Researchers at Lobachevsky University have discovered the TrkB receptor system's role in forming neural networks. Activating this system increases complex functionally active neural networks with high transmission efficiency of nerve impulses, essential for learning and memory.
Researchers challenged a popular idea about how machine learning algorithms think by applying information theory to classification problems. They found that classifiers with many layers do not necessarily trade off between prediction and compression as previously thought.
Researchers at Max Planck Florida Institute for Neuroscience developed a new method to identify functional properties of individual synapses linking the two hemispheres. They found that callosal inputs and local inputs with similar orientation preference are clustered within the dendritic field, enabling coordinated network activity.
A new software, idtracker.ai, can identify up to 150 individual fish with high accuracy, extracting valuable data for understanding group behavior. The AI-powered system uses deep learning neural networks and conventional algorithms to recognize unique features of each zebrafish.
A new study led by Salk Institute researchers challenges the long-held view that individual brain cells operate as filters. The study found that the same neurons can prefer coarse or fine details depending on the context, and teaming up endows networks of neurons with flexibility to adapt to changing conditions.
Galanin-like peptide (GALP) plays a key role in regulating feeding behavior and energy metabolism. Recent research has shown that GALP administration leads to decreased respiratory quotient, indicating accelerated lipid metabolism.
Researchers developed LEAP, a neural network-based system that tracks individual body parts in video footage, enabling the study of animal behavior and neural processes. The tool has great potential outside of neuroscience, automating labor-intensive analysis to study diverse animal locomotor behaviors.
Researchers have developed a neural network that can accurately detect age and gender from video frames, outperforming existing convolutional neural networks. The new method aggregates confidence levels using mathematical statistics and Dempster-Shafer theory, enabling the detection of age and gender with improved accuracy.
A team of researchers developed a neuroinspired hardware-software co-design approach that can make neural network training more energy-efficient and faster. The approach uses a type of energy-efficient neural network called spiking neural networks, combined with the soft-pruning algorithm to minimize computing power and time.
The study combines synfire communication, coherence and resonance to provide insight into how messages are exchanged between brain areas. The researchers found that oscillations play a significant role in determining whether communication can take place.
Researchers found that isolating symptoms to a single brain area resulted in low reproducibility rates, but analyzing symptom-specific circuitry within brain networks led to 100% reproducibility. This new approach aims to shed light on therapy development by identifying common symptom-localization patterns across different diseases.