Articles tagged with Neural Networks
A new theory by Max Ortiz Catalan suggests that phantom limb pain results from neural entanglement between the missing limb's circuitry and pain perception networks. The 'stochastic entanglement' hypothesis explains how a novel treatment, Phantom Motor Execution (PME), can help alleviate pain by reactivating dormant brain areas.
Researchers at the University of Waterloo have developed a new AI-powered system that enables TVs to understand voice queries more accurately. The system, which was tested on Comcast's Xfinity X1 platform, can handle complex queries and personalize results based on user context.
A novel encryption technique combining homomorphic encryption and garbled circuits secures data used in online neural networks without significantly slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data.
Researchers use deep neural networks to recognize images transmitted over optical fibers, achieving high accuracy despite distortions caused by environmental factors. The technique has potential for improving endoscopic imaging in medical diagnosis and increasing the information-carrying capacity of fiber-optic telecommunication networks.
Researchers found that brain activity patterns shift towards stored representations of clear images, suggesting that past experiences play a significant role in perception. The study used fMRI to analyze how the brain processes blurred images and found that higher-order circuits were more affected by clear image-induced shifts.
Researchers at NIST have developed a silicon chip that uses light instead of electricity to precisely distribute optical signals across a miniature brain-like grid. The chip enables complex routing schemes necessary to mimic neural systems and has demonstrated uniform output with low error rates.
The Network for Excellence in Neuroscience Clinical Trials has been renewed for five more years, enabling the study of new treatments for brain disorders. Nine clinical trials are currently underway, demonstrating the potential of NeuroNEXT to expedite research and bring treatments to patients faster.
Researchers found that experienced animals form memories using different plasticity mechanisms than naive subjects, suggesting the way our neurons form new connections depends on their prior history. Previously activated neurons were more excitable, making them capable of different kinds of plasticity.
Researchers trained a machine learning algorithm to analyze microscopic radiation damage, achieving an accuracy of 86% compared to humans. The algorithm can process images faster and more efficiently than humans, making it a promising tool for developing safe nuclear materials.
Artificial neural networks can now be trained directly on an optical chip, paving the way for less expensive, faster, and more energy-efficient AI. This breakthrough enables complex tasks like speech or image recognition to be performed more efficiently.
Scientists developed a neural network device using nanomaterials, generating spontaneous spikes similar to nerve impulses of neurons. The researchers replicated brain function by utilizing molecular junctions and negative differential resistance.
Researchers at UC San Diego have found that axon geometry is crucial in information flow, with a 'refraction ratio' of 0.92 indicating optimal balance between signal latency and refractory period. This discovery has implications for understanding neurological disorders like autism and developing more brain-like artificial neural networks.
Researchers at the Higher School of Economics have developed a new method for recognizing people on video using only one photo, achieving higher recognition accuracy compared to existing methods. The algorithm uses information on how reference photos are related to correct errors in video frame recognition.
Researchers at Caltech developed an artificial neural network made of DNA that can accurately identify handwritten numbers. The network, designed by Kevin Cherry, uses a 'winner take all' competitive strategy and undergoes complex reactions to classify molecular information.
Researchers developed a means of tracking retinal neuron activity as it delivers visual information to the thalamus, revealing organized clusters and shared sensitivities among different types of neurons. This finding suggests the retina's version of Pointillism, where nearby dots fuse together to create diverse colors.
Researchers analyzed data from three major brain banks and found that human herpesvirus DNA and RNA were more abundant in the brains of Alzheimer's patients. The study suggests that viruses may be involved in regulating genes associated with increased Alzheimer's risk, and could offer potential new paths for treatment.
Scientists at PNNL have developed a deep neural network that accurately detects nuclear events with high accuracy, often exceeding human expert's performance. The network was trained on 32,000 pulses and achieved impressive results, correctly identifying 99.9% of signals with minimal noise.
Researchers at MIT have developed an AI-based method to design multilayered nanoparticles with desired properties, potentially speeding up the development of new materials. The technique uses computational neural networks to learn how a nanoparticle's structure affects its behavior, allowing for faster prediction and design.
Scientists have developed a neural network that can recognize features in x-ray absorption spectra sensitive to atomic arrangement at fine scales. This method helps reveal details of atomic-scale rearrangements during iron's phase transition, and could be applied to study nanoparticles, catalytic materials, and other materials.
Researchers at The University of Tokyo Institute of Industrial Science describe a new method for creating one mini neuron network model, using microscopic plates to connect neurons together one cell at a time. This approach guides neurons to grow in a defined way and form functional communication hubs.
Houston Methodist researchers developed a lab-on-a-chip technology that models human neural networks to study retinal diseases and potential treatments. The NN-Chip can quickly screen drugs for damaged neuron and retinal connections, offering new hope for treating conditions like macular degeneration.
A groundbreaking study by Gladstone and Google AI uses deep learning to analyze cell images, identifying features that humans can't detect. The method uncovers important information that was previously impossible or problematic for scientists to obtain.
Bartos' project will examine the functional role of inhibitory nerve cells in forming memory traces and controlling cognitive behavior, a process not yet fully understood.
A smartwatch coupled with a machine learning algorithm detected atrial fibrillation (AF) with high accuracy in patients undergoing treatment for AF. The study used data from 9,750 participants and found promising results for the use of commercially available smartwatches to detect AF.
A research team led by Eiji Watanabe reproduced illusory motion using deep neural networks trained for prediction. The DNNs accurately predicted motion in unlearned videos and represented rotational motion in illusion images, similar to human visual perception.
ORNL researchers design a novel method for energy-efficient deep neural networks, achieving nearly the same accuracy as original DNNs while consuming 38 times less energy. The approach uses 'deep spiking' neural networks with stochastic-based implementation, which overcomes tradeoff between energy efficiency and task performance.
A new algorithm enables larger parts of the human brain to be represented using the same amount of computer memory, significantly reducing the memory required for simulations. This breakthrough allows researchers to simulate neuronal networks on the scale of the human brain for the first time, enabling studies of complex brain functions.
Researchers used deep learning to analyze patterns of taxi demand and predict demand significantly better than current technology. This approach could help lessen idle time for taxis, making cities cleaner and improving safety in congested areas.
Researchers recorded electrical activity of hundreds of neurons in a mouse model for up to half an hour, discovering competing neural networks that operate at different timescales. The findings show that certain networks can synchronize their activity, while others slow down or speed up in a coordinated manner.
A novel 'memtransistor' device developed by Northwestern University's Mark C. Hersam can process information and store memory like the human brain, potentially revolutionizing computing. The memtransistor combines characteristics of a memristor and transistor, operating with multiple terminals similar to neural networks.
Lobachevsky University scientists discovered that GDNF protects cultures from cell death and maintains network activity during hypoxia. The neurotrophic factor partially negates the consequences of hypoxia by influencing synaptic plasticity.
MIT researchers developed a special-purpose chip that increases the speed of neural-network computations while reducing power consumption. The chip can calculate dot products for multiple nodes in a single step, improving efficiency and making neural networks more practical for handheld devices.
Researchers created a computer model using neural networks to predict areas prone to corruption, finding that longer government terms and certain economic variables increase the likelihood. The study aims to contribute to anti-corruption efforts by targeting high-risk regions.
Researchers studying nervous system adaptation to ischemic damage hope to develop effective therapeutic strategies by understanding how neural networks function under stress. They have developed methods for modeling different phases of ischemia and studied the features of neural network operation under such effects.
Researchers developed codes MENNDL and RAVENNA to efficiently design and train neural networks, generating and training up to 18,600 networks simultaneously. This enables the training of highly accurate networks in a fraction of the time, with applications in self-driving cars, intelligent robots, and scientific experiments.
A new noninvasive approach to treat tinnitus has shown promising results in a double-blind study, alleviating symptoms in 20% of participants. The therapy involves alternating audio and somatosensory stimulation, delivered through headphones and mild pulses on the neck or cheek.
Researchers from HSE and University of Valladolid created a neural network prediction model to detect corruption cases in Spanish regions. The model uses macroeconomic and political determinants to estimate the probability of corrupt cases emerging over three years, providing valuable insights for anti-corruption measures.
Researchers at the University of Michigan have created a new type of neural network made with memristors that can dramatically improve the efficiency of teaching machines to think like humans. The system, called reservoir computing, uses fewer nodes and requires less training time than traditional neural networks.
A new study shows that very low levels of electrical stimulation can instruct an appropriate response or action in the brain, bypassing damaged senses. The findings have significant implications for the development of neuro-prosthetics and brain-computer interfaces.
A study published in eLife reveals that certain mammalian neurons have shapes and electrical properties well-suited for deep learning. The algorithm simulates how these neurons collaborate to achieve deep learning, offering a more biologically realistic approach.
Researchers have developed a new writing method to create any desired magnetic pattern on nanowires, mimicking brain information processing. This breakthrough could lead to the creation of hardware neural networks, which may surpass software-based approaches in efficiency.
Researchers at Lobachevsky University are developing a neural network prototype based on memristors that can analyze and classify living cell culture dynamics. The project aims to create compact electronic devices that function as part of bio-like neural networks in conjunction with living biological cultures.
Researchers efficiently used Stampede2's 1024 Skylake processors to complete a 100-epoch ImageNet training with AlexNet in 11 minutes, setting the fastest time recorded to date. The Layer-Wise Adaptive Rate Scaling (LARS) algorithm enabled this breakthrough, allowing for larger-than-ever batch sizes and adaptive learning rate adjustments.
Researchers at Osaka University designed a novel gait recognition method that can overcome intra-subject variations by view differences. The proposed architectures outperformed state-of-the-art benchmarks in accordance with their suitable situations of verification/identification tasks and view differences.
Researchers at Northeastern University's Center for Complex Network Research have identified fundamental rules for how the brain controls movement in nematode worms. The study provides unprecedented detail on how individual neurons control specific types of locomotion, paving the way for future research into human brain function and ne...
Researchers from Lehigh University and Columbia University have developed a new testing approach for deep learning platforms used in self-driving cars, malware-detection, and other systems. Their method, called DeepXplore, exposes thousands of unique incorrect corner-case behaviors, enabling faster identification and fixing of errors.
A new approach brings transparency to self-driving cars and other self-taught systems by automatically error-checking neural networks. Researchers found thousands of bugs missed by previous techniques, activating up to 100% of network neurons and improving accuracy up to 99%.
Researchers have developed an AI-powered technology that can decode what the human brain is seeing by analyzing fMRI scans from people watching videos. The breakthrough could lead to new insights into brain function and improve artificial intelligence.
Researchers developed an algorithm based on ant trail networks, which adapts to changes in the environment and avoids taking the shortest path. The algorithm is inspired by how ants navigate through complex vegetation and repair broken trails using a 'greedy search' method.
Nerve cell networks reorganize themselves during periods of inactivity, becoming hypersensitive and prone to overreaction when signals are reinstated. Researchers developed a high-speed microscopy process to visualize communication networks of living neurons, shedding light on the effects of blocking neural pathways.
A neuroimaging study reveals that blind individuals perform better on a touch discrimination task when their hands are crossed due to stronger frontal-parietal connectivity. In contrast, sighted individuals show greater activity in parietal and premotor areas with uncrossed hands.
The study seeks to discover calcium sources in synapses that prolong neurotransmitter release, a process more expressed in neurodegenerative diseases. Researchers will investigate key players in this process using asynchronous release of neurotransmitters.
Researchers develop a general-purpose technique to analyze neural networks trained for natural-language-processing tasks. The method applies to any black-box text-processing system, revealing idiosyncrasies in human translators' work and identifying gender biases in machine translation systems.
Researchers discovered a new learning mechanism that spans seconds, allowing for the storage of entire sequences of events, including places traversed. This finding challenges the widely accepted Hebbian learning rule, suggesting no causal relationship between interconnected neurons is required to form long-lasting associations.
Researchers from SLAC and Stanford used neural networks to analyze images of strong gravitational lensing, performing complex analyses in a fraction of a second. The technique has the potential to transform astrophysics by analyzing vast amounts of data quickly and automatically.
The Journal of Applied Remote Sensing has awarded three exceptional articles for their outstanding contributions to remote sensing research and applications. The winning articles focus on ice cloud measurement, a neural-network architecture for scene classification, and through-wall imaging.
Researchers developed neural networks to evaluate short narratives, improving predictions over a baseline system. The AIs classified texts into popular and non-popular categories, highlighting the importance of understanding story structures in narrative evaluation.
Machine learning scientists at Disney Research developed a dynamic word embeddings model that uncovers how the meanings of words change over time. The model, which integrates neural networks and statistics used in rocket control systems, detects semantic change throughout history by analyzing semantic vector spaces.
Researchers have developed a method for designing energy-efficient neural networks, reducing power consumption by up to 73% compared to standard implementations. The new approach uses an analytic tool to evaluate and prune low-weight connections, resulting in more efficient networks with fewer connections.
Researchers developed a new algorithm that can turn audio clips into highly-realistic videos of people speaking, using available public domain video footage. This technology has potential applications in improving video conferencing and creating realistic virtual reality experiences.