Articles tagged with Neural Networks
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.
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.
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.
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.
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.
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 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 MIT have developed a new architecture for optical neural networks, which can perform complex linear algebra operations using light signals. The new design eliminates uncorrectable errors that limited the scalability of earlier systems, enabling larger networks with improved accuracy.
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.
The researchers have developed an AI algorithm called M3GNet that can predict the structure and dynamic properties of any material. The algorithm was used to create a database of over 31 million yet-to-be-synthesized materials with predicted properties, facilitating the discovery of new technological materials.
Engineers at Tokyo Tech demonstrated a simple approach to improve AI classifier training using limited sensor data, increasing quality without extra cost. The proposed method promises to address the challenge of classification accuracy in real-world applications, where reliable answers are crucial.
Chung-Ang University researchers propose a new algorithm, MR-UCB and MR-APE, to tackle stochastic multi-armed bandit problems with heavy-tailed noise distributions. The methods guarantee minimal loss for worst-case scenarios with minimal prior information.
A Rutgers-led study found that a gene mutation associated with autism causes an overstimulation of brain cells, disrupting the normal information flow. The researchers used human stem cells and transplanting them into mouse brains to understand how the mutation affects brain development.
Children's brains exhibit rapid GABA boosts during learning sessions, allowing for efficient consolidation of new information. Adults' brains, on the other hand, struggle to stabilize learned knowledge due to reduced GABA levels, leading to slower learning rates.
A novel AI-based malware detection and classification system has been developed for 5G-enabled Industrial Internet of Things (IIoT) systems. The system achieved an accuracy rate of 97% on benchmark datasets, enabling the secure connection of applications such as smart cities and autonomous vehicles.
Researchers at CABBI used unmanned aerial vehicles with machine learning methods to select the best candidate genotypes in miscanthus breeding programs. The new method leverages high-resolution aerial imagery and three-dimensional neural networks to estimate crop traits such as flowering time, height, and biomass production.
Researchers developed an algorithm to extract bouton-like structures from calcium imaging data, identifying synchronized synapses during fictive locomotion. PQ-clustering outperformed other algorithms in mimicking synaptic activity patterns.
The US Department of Energy's Oak Ridge National Laboratory has developed a massive geographic dataset, USA Structures, using deep learning to forecast potential damage and accelerate emergency response. The dataset provides critical information on building outlines and attributes, enabling FEMA to prioritize response efforts.
Researchers at MIT have developed a machine-learning model that captures how sounds propagate through spaces, allowing for accurate visual renderings of rooms. This technique has potential applications in virtual and augmented reality, as well as improving AI agents' understanding of their environment.
A new project aims to create more race-inclusive AI in medicine by developing a distributed, inclusive data collection and learning framework that relies on smartphone apps. The framework uses federated learning, which allows models to be trained on device data while protecting user privacy.
Researchers from University of Warsaw create spiking neuron using photons to mimic biological brain's behavior. This achievement paves the way for photonic neural networks that process information faster and more efficiently than conventional systems.
Researchers at the University of Copenhagen have made a breakthrough in understanding schizophrenia by analyzing individual brain cells. The study identified specific neurons and networks affected by the disease, suggesting that targeting these areas could lead to new treatment options.
Researchers used a neural network model to study how brain regions interact during sleep. They found that the hippocampus and neocortex work together to convert fleeting information into long-term memory. The findings suggest that alternating between REM and slow-wave sleep stages is crucial for strong memory formation.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers created a 3D electrode array that maps the locations and activity of up to 1 million potential synaptic links in living brains. The system uses recordings of millisecond-scale evolution of electrical pulses in tens of thousands of neurons, allowing for dense and accurate mapping of brain circuits.
A new multimodality prediction method jointly learned vision and sequence information for accurate solar wind speed prediction. The model achieved best performance in many metrics, including RMSE, MAE, and CORR, and demonstrated the effectiveness of each module in improving prediction accuracy.
A new CNN framework, PE-Net, is proposed for predicting machine remaining useful life (RUL) accurately. The framework uses a novel architecture with small-sized one-dimensional convolution kernels and deep networks to learn features from input time series signals.
Scientists have discovered a hidden structure in the connections between neurons in the brain, which is crucial for the stability of the neuronal network. By combining mathematical models with experimental recordings, researchers found that the relative ratios of connection strengths are more important than absolute values.
Scientists discovered a novel neural mechanism that accompanies unconsciousness, masking sensory inputs with spontaneous activity. The auditory cortex's response to sounds is indistinguishable from its own internal activity under anesthesia.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
Physicists at the University of Basel have developed a computational shortcut for neural networks, allowing for faster calculation of optimal solutions without training. This breakthrough provides insight into neural network functioning and could help detect unknown phase transitions in materials and quantum systems.