Articles tagged with Neural Networks
Feng Xiong is developing a two-dimensional synaptic array to enable computers to process vast datasets with less power and greater speed. This technology aims to mimic the brain's efficient learning process, allowing for more precise adjustments between states.
Using simulated silicon neurons, researchers found that energy constraints can lead to a dynamic, at-a-distance communication protocol more robust and energy-efficient than traditional computer processors. This protocol enables computing on a secondary network of spikes, allowing for efficient communication and processing.
A team of scientists proposes a memristive neurohybrid chip to create compact biosensors and neuroprostheses with high adaptability. The system combines neural cellular and microfluidic technologies for real-time registration, processing, and stimulation of bioelectrical activity.
An international team developed artificial neurons that can precisely target specific brain cells using optogenetics and light patterns. This technology has the potential to replace damaged brain circuits and restore communication between brain regions.
Researchers developed Early Bird, an energy-efficient method for training deep neural networks, which can use 10.7 times less energy than traditional methods to achieve the same level of accuracy. This breakthrough could lead to significant cost savings and a reduction in greenhouse gas emissions.
Research reveals that early visual experience drives precise alignment of cortical networks to unite inputs from both eyes, enabling unified binocular representation. This process occurs within the first week after eye opening and refines neuron response properties.
A new standard for machine learning in telecommunications networks has been approved, enabling faster data transmission rates of up to 20 Gbps and reducing latency to less than 5ms. This breakthrough is made possible by the application of deep learning techniques, allowing for complex pattern recognition and network load management.
New research reveals that certain internal clock neurons in fruit flies, previously thought to send time-keeping cues to the brain, actually receive cues from the external environment. This finding has significant implications for understanding circadian rhythm disruptions and their associated health problems.
Researchers at MIT developed a new automated AI system that reduces the energy required for training and running neural networks. The system, called a 'once-for-all' network, trains one large neural network comprising many pretrained subnetworks, reducing carbon emissions by low triple digits.
Researchers found that rapid-acting antidepressants share the ability to regulate both synaptic potentiation and reciprocal homeostatic mechanisms, which weaken synaptic strength during sleep. This suggests that slow-wave responses could be a useful measure for determining treatment efficacy and developing novel treatments.
Researchers propose a new framework to quantify the predictability of temporal networks, which encodes the ordering and causality of interactions between nodes. The study found that the contributions of topology and temporality to network predictability vary significantly across different types of real networks.
Researchers at the University of Texas at Austin developed a method to make big data processing more energy efficient using magnetic components. By leveraging lateral inhibition in artificial neurons, they achieved an energy reduction of 20-30 times compared to standard back-propagation algorithms.
Researchers at MIT used machine learning to streamline the discovery process for new materials, narrowing down 3 million candidates to eight promising options in just five weeks. The neural network was able to predict properties and optimize criteria, improving upon conventional analytical methods.
Research in mice reveals that lactation temporarily changes how a mother's TIDA neurons regulate prolactin secretion, causing them to fire more frequently and out of rhythm. However, these changes are fully reversible after weaning, suggesting a unique adaptation to motherhood.
The Learning to Synthesize (LS-DNN) approach splits input signals into low and high spatial frequency bands, enabling deep neural networks to process and synthesize them. The algorithm is robust in handling noisy intensity signals, making it suitable for applications like x-rays and sonograms.
Researchers developed a computer program to identify each nerve cell in fluorescent microscope images of living worms, overcoming previous challenges by creating unique genetic modifications. The program uses a mathematical algorithm to analyze images and assign neuron identities based on position variations between individual animals.
Researchers developed a platform to coculture neurons and muscle cells, capturing the emergence of neuromuscular junctions and synchronized bursting patterns. The study provides new insights into biohybrid machines and their potential applications in fields like intelligent drug delivery and environment sensing.
A team from Deakin University in Australia developed an improved sight-correcting system for self-driving vehicles. By watching human operators complete tasks, the vehicles can learn to make decisions based on visual information, reducing the need for extensive training data.
A new chip has been developed at TU Wien that can recognize certain objects within nanoseconds, leveraging artificial intelligence and a special material. The chip integrates the neural network with its AI directly into the image sensor, making object recognition faster by many orders of magnitude.
Researchers found that brain cells generate a 'new song' with the same beat for each breath, adapting to changing rhythms throughout the day. The discovery could lead to new approaches to treating breathing disorders and may even help combat opioid-related deaths.
Researchers successfully separate and observe single-molecule magnets (SMMs) on a magnetically neutral silica substrate using transmission electron microscopy. This breakthrough enables the development of auto-associative memories and multi-criterion optimization systems, mirroring the human brain.
Researchers at MIT have developed a machine learning method to fill in the missing low-frequency seismic waves in human-generated seismic data, allowing for more accurate mapping of underground structures. The technique was trained on simulated earthquakes and used to infer missing frequencies from new input data.
A multidisciplinary study led by UB researchers has developed a new experimental tool to study how neuronal networks recover their function after neuron loss. The study shows that the network quickly activates self-regulation mechanisms that reinforce existing connections and restore circuit functionality.
Researchers at Mayo Clinic have created an artificial intelligence (AI) algorithm that can detect unseen characteristics of hypertrophic cardiomyopathy using standard EKGs. The AI's ability to diagnose the disease was found to be highly accurate, with an area under the curve of 0.96, outperforming traditional tests.
Researchers created a digital amplifier model using a deep neural network that can accurately simulate the sound of various guitar amplifiers, including popular brands like Marshall and Orange. The study uses black-box modelling to replicate the observed input-output mapping of analogue circuitry.
Researchers created a neural network that autonomously finds solutions well-adapted to quantum advantage demonstrations, aiding in developing new efficient quantum computers. This breakthrough enables the prediction of quantum advantages in complex networks, which is crucial for creating cost-effective and reliable quantum devices.
Scientists have identified recurring patterns in brain neurons that can be used to explain their behavior and function, paving the way for creating artificial intelligence that mimics the human brain. By understanding these patterns, researchers aim to develop new treatments for neurological disorders and improve current technology.
A deep learning method using a convolutional neural network (CNN) accurately differentiates between malignant and benign solid masses in small renal masses on contrast-enhanced CT scans. The corticomedullary phase showed the highest AUC value, indicating its effectiveness in malignancy prediction.
Researchers develop 'flash and freeze' method to study structure and function of synapses in intact neural circuits. The method allows for simultaneous observation of structural changes during signaling, revealing a near-identity between structurally and functionally defined vesicle pools.
Researchers developed a system to accurately detect space debris in Earth's orbit using laser ranging telescopes and neural networks. The new algorithm significantly improves the success rate of space debris detection, allowing for safer spacecraft maneuvers.
Scientists at Tokyo Institute of Technology found that overly strong connections can invert the effect of connectivity on complex activity, leading to more regular patterns. This phenomenon is observed in various natural and engineered systems, including neurons, coupled oscillators, and wireless terminals.
Researchers are testing whether changing brain's fuel source from glucose to ketones could potentially save neurons and neural networks over time. The study, funded by a $2.5 million grant, aims to understand how ketones affect brain cells and connectivity in the face of insulin resistance.
Researchers developed an innovative method to measure the complexity of image representations in deep neural networks, shedding light on their processing stages. The study found that classification accuracy depends on the network's ability to simplify information, with more accurate results from simplified representations.
A new method accelerates template creation for medical-image analysis, generating brain scan templates based on patient attributes such as age and sex. The model can synthesize atlases from sparse data, improving disease diagnosis accuracy.
Researchers from Russia and Germany discovered that activation of GDNF helps protect brain cells from death during hypoxic damage, maintaining neural network activity. This finding can lead to the development of an effective method for correcting CNS pathologies developing under oxygen deficiency.
Researchers at Argonne National Laboratory have developed domain-aware neural networks to replace expensive parameterizations in the Weather Research and Forecasting (WRF) model. These algorithms can predict environmental data more accurately with significantly less training data, enabling faster and higher-resolution simulations.
Researchers used deep neural networks to analyze ECG test results from over 2 million patients, identifying those at high risk of developing atrial fibrillation or dying within a year. The models were found to be superior in predicting mortality risk even in patients with normal ECGs.
Researchers developed a novel computational approach using deep artificial neural networks to predict neural responses to images. The study found that certain stimuli, such as checkerboards or sharp corners, elicit strong responses from neurons, contradicting current dogma in the field.
Researchers at Duke University have trained an AI tool to identify up to 200 species of birds from just a photo. The system, which uses deep learning, also shows its thinking by highlighting key patterns in the image.
Researchers developed an AI-based ozone forecasting system that can predict ozone levels with 85-90% accuracy. The model uses convolutional neural networks to analyze current conditions and forecast future ozone levels, improving health alerts for people at risk.
A new theory suggests that consciousness arises from synchronized neural activity and is guided by thermodynamic principles. During conscious states, the brain has higher entropy and more connected neural networks, leading to greater mental flexibility.
A team of researchers has successfully mapped the local connectome in the cerebral cortex using 3D electron microscopy, producing a connectome about 26 times larger than previous ones. The study provides insights into the density and magnitude of neuronal networks in the brain.
Researchers have developed an artificial intelligence technique that uses deep neural networks to analyze data from experiments on nanoscale ferroelectrics. This method has identified geometrically-driven differences in ferroelectric domain switching, providing new insights into the mechanisms of ferroelectric switching.
Researchers developed a neural network, PatchFCN, trained on 4,396 CT scans to detect brain hemorrhage abnormalities with accuracy similar to human experts. The algorithm achieved high accuracy and pixel-level delineation, classifying abnormalities into different pathological subtypes.
Researchers developed lipid-based memcapacitors that mimic biological synapses, accelerating routes to neuromorphic computing. The discovery could support the emergence of biology-inspired computing networks for sensory approaches to machine learning.
Researchers at the University of Delaware are developing new memory devices that can support neural networks in low-power embedded systems. These advancements aim to improve the lifetime and reliability of IoT devices, which currently struggle with battery power and memory constraints.
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.
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 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.
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.