Articles tagged with Neural Modeling
The 3rd annual Frontera User Meeting showcased the power of the NSF-funded supercomputer in various domains of science. Researchers presented findings on projects utilizing Frontera's capabilities, including compound storm surge models and nonlinear earthquake simulations. Additionally, scientists leveraged the system to analyze anonym...
Researchers found that deep neural networks often respond the same way to images with no resemblance to the target, generating unnatural signals. The models develop unique invariances that are different from human perceptual systems, causing them to perceive pairs of stimuli as similar despite their differences.
Researchers assembled an atlas of hundreds of cell types that make up a human brain in unprecedented detail. The study uses techniques originally developed for mice to identify brain cell subtypes in human brains.
Researchers developed an easy-to-use optical chip that can configure itself for different functions, enabling optical neural network applications. The chip achieves positive real-valued matrix computation and demonstrates optical routing, low-loss light energy splitting, and matrix computations.
Researchers at Mount Sinai Hospital developed an algorithm called HistoAge that predicts age at death based on human brain tissue specimens using artificial intelligence, revealing insights into aging mechanisms and neurodegenerative diseases like Alzheimer's disease.
Researchers developed AI tools to analyze speech patterns in people with schizophrenia and found that these patterns can be distinguished from those of healthy individuals. The study uses an AI language model trained on internet text to represent the meaning of words, and it was able to predict the words generated by control participan...
A new study reveals that changing nutrient use can reprogram immune cells, potentially treating cancer and infections. By blocking choline metabolism, researchers found a 'tremendous reprogramming of the immune profile' in mice, suggesting this knowledge could lead to novel therapies.
Researchers found that administering precursors of Elovanoids improved neurological deficit in an experimental model of ischemic stroke. The study identified a cascade of gene responses and sheds light on potential new therapeutic avenues for treating ischemic strokes.
A new AI model integrates imaging and non-imaging patient data for improved diagnostic performance on chest X-rays. The multimodal model outperformed other models for diagnosing up to 25 conditions, showing potential as an aid to clinicians in high-pressure diagnoses.
A recent study published in Radiology: Artificial Intelligence found significant racial and sex-related biases in an AI chest X-ray foundation model, affecting its performance across patient subgroups. The researchers highlighted the need for comprehensive bias analysis to ensure diversity and representativeness in dataset collection.
GlowTrack, a non-invasive movement tracking method using fluorescent dye markers, improves the capture of diverse movements in laboratories. This technique enables easier comparison of movement data between studies, increasing scientific discovery and advancing fields like biology, robotics, and medicine.
A recent study published in Journal of Advertising sheds light on the effects of in-stream video advertising on ad information encoding. The research found that mid-roll ads elicit negative emotions but do not affect viewers' purchase intention. In contrast, pre- and post-roll ads have a significant impact on memory formation, highligh...
Monash University researchers have developed a new capacity to map and model the spread of brain changes in people with different stages of psychoses. The study identified the hippocampus as a possible early site of brain changes in psychosis, which could guide therapies targeting this area.
The Open Encyclopedia of Cognitive Science (OECS) is a dynamic web reference that will equip readers with essential tools to grapple with the profound implications of cognition and intelligence in today's society. With generous funding from James S. McDonnell Foundation and the Allen Institute for AI, the first set of articles will be ...
Kevin Moran and Ziyu Yao aim to improve neural language model interpretability to help developers understand why AI-powered tools make predictions. The project will produce educational materials on best practices and focus on recruiting underrepresented computer science students.
Researchers from Radboud University have successfully decoded brain signals into intelligible speech using a combination of implants and advanced artificial intelligence models. The technology has been tested with an accuracy of 92-100%, showing promise for individuals with locked-in states who are paralyzed and unable to communicate.
A hybrid system of electronic encoding and diffractive optical decoding transmits optical information with high fidelity through random, unknown diffusers. The system outperforms traditional approaches that only utilize a diffractive optical network or an electronic neural network for optical information transfer.
Researchers at Massachusetts General Hospital have developed a novel 3D human cellular model that mimics the intricate interactions between brain cells and immune invaders, providing insights into how immune cells contribute to Alzheimer's disease progression. The study identified specific types of immune cells called CT8+ T Cells surg...
Researchers found a lognormal distribution of neuron densities in mammalian brains, influencing network connectivity and potentially promoting efficient information transmission. The discovery is relevant for modeling the brain accurately and designing brain-inspired technology.
Researchers developed a new mathematical model to study circadian rhythm resilience and develop ways to improve it in individuals with weak internal clocks. Sustained disruptions can lead to disorders like diabetes and memory loss.
Scientists successfully reconstructed recognizable versions of Pink Floyd's 'Another Brick in the Wall, Part 1' using nonlinear modeling to decode brain activity. The study identified a unique region in the Superior Temporal Gyrus (STG) responsible for rhythm perception, with electrodes from this region crucial for accurate reconstruct...
Researchers propose a hypothesis that astrocytes, non-neuronal cells in the brain, can perform core computation as transformers, providing insights into human brain function and machine learning success. This discovery could spark future neuroscience research and help explain transformer performance across complex tasks.
Researchers studied how tastes influence creativity by analyzing brain networks and individual preferences. They found that the subjective evaluation of ideas plays a crucial role in creativity, and that individuals have different creative profiles related to their fields of activity.
Researchers found an abnormal imbalance of excitatory cortical neurons in people with autism spectrum disorder, depending on their head size. The study used human 'mini-brain' models called organoids to recreate the brain development alteration that occurred in patients during fetal development.
Researchers created a self-supervised AI model called GedankenNet that learns physics laws and thought experiments to reconstruct microscopic images. The model successfully reconstructed human tissue samples and Pap smears from holograms without relying on real-world experiments or data.
Researchers used a mathematical theory called the free energy principle to predict how real neural networks learn and organize themselves. The study successfully mimicked this process in rat embryo neurons grown in a culture dish, demonstrating the principle's guiding force behind biological neural network learning.
Researchers developed an AI model called OncoNPC that can analyze genetic data to predict cancer type and origin. The model accurately classified at least 40% of tumors with unknown origin, leading to a 2.2-fold increase in eligible patients for targeted treatments.
A new open-source software, NMSM Pipeline, enables clinicians and engineers to create personalized computer models of patient movement to optimize treatment designs. The software uses physics-based models to predict and optimize functional outcomes for patients with various mobility impairments.
A team of Lithuanian researchers has created an AI-based system to facilitate the rehabilitation process for stroke patients. The system, which uses wearable equipment and electromyography (EMG) technology, enables patients to track their progress and receive feedback on their exercises.
A new geometric deep learning model called GFCN has been developed to detect stroke lesions in brain imaging scans. The model leverages rich geometric information to segment brain tissue and achieves higher segmentation performance than other neural network architectures.
A University of Ottawa study reveals that a diverse brain's ecosystem is key to maintaining normal function while responding to changes. This concept is inspired by Charles Darwin's idea that biodiversity is crucial for survival, suggesting cell-to-cell diversity helps prevent failures in brain circuits.
Research team at USTC identifies impaired loss aversion and altered functional connectivity in IGD patients. The study's findings could contribute to the diagnosis and treatment of this disorder by exploiting changes in edge-centric brain networks.
Researchers developed a deconvolution method for epidemiology using neural networks, inferring daily infection rates from mortality data. The approach can assess the effectiveness of non-pharmaceutical interventions like lockdowns and mask mandates in reducing infection transmission.
Scientists have created human brain organoids free of animal cells, which could greatly improve the study and treatment of neurodegenerative conditions. The novel method uses an engineered extracellular matrix to support stem cell growth, resulting in more accurate models of brain development.
A Lehigh University professor has received $4 million in NIH grants to develop an AI-driven approach for precision mental health diagnosis and care. The project aims to identify biomarkers in the brain that can predict treatment response and personalize interventions for patients with depression and other mental disorders.
The study combines real and robotic insects to understand how they sense forces in their limbs while walking. Campaniform sensilla (CS) are force receptors found in insect limbs that respond to stress and strain, providing critical information for controlling locomotion.
Researchers demonstrate that AI language models like ChatGPT can generate high-quality fraudulent medical articles with standard sections and references. The study highlights the need for increased vigilance and enhanced detection methods to combat potential misuse of AI in scientific research.
A team of researchers from Kyoto University and international institutions has developed a mathematical solution to the temporal asymmetry of nonequilibrium disordered Ising networks. This breakthrough offers insights into the behavior of biological systems, machine learning, and AI tools.
A team of scientists identified the dorsal medial prefrontal cortex as a key region in predicting rumination, which is linked to depression. The study's findings suggest that dynamic connectivity between brain regions can be used to decode rumination patterns.
Researchers used a stem cell model to study the effects of Alzheimer's disease-associated mutations on early human brain development, finding that mutant spheres were larger and contained fewer mature neurons. The study highlights the need for tailored therapies and paves the way for studying Alzheimer's in its early stages.
Researchers developed a new method for controlling lower limb exoskeletons using deep reinforcement learning, enabling more robust and natural walking control. The system has the potential to benefit users with spinal cord injuries, multiple sclerosis, stroke, and other neurological conditions.
A new study suggests that simple limitations in neural responses explain visual illusions, rather than deeper psychological processes. Researchers developed a model that combines information on neural firing speeds, pattern perception, and natural scene assumptions to predict how animals see colour and understand visual illusions.
Researchers created a detailed 3D image of the synapse, a key juncture in neuronal communication. The model reveals the precise geometry of interactions between individual cells, which may hold the key to understanding neurodegenerative diseases.
Researchers developed a new spectropolarimetric imaging technique called DIP-SP, which integrates a passive polarization modulator into an imaging spectrometer. This approach enables high-dimensional information capture from incomplete measurements and significantly improves image quality.
Researchers at MIT have designed a computational model that can predict other people's emotions, including joy, gratitude, and regret. The model uses insights into human intuition, incorporating factors such as desires, expectations, and observation of actions to make predictions.
Researchers used mouse genetics to determine if brain or spinal cord causes dystonia, finding that spinal cord is responsible. Spinal cord dysfunction leads to signs of dystonia similar to those seen in humans, providing a new target for treatment.
Researchers propose a deep neural network-based method for calibrating 4-quadrant analog solar sensors, reducing errors by up to 0.25° (3σ). The approach uses cubic surface fitting and deep feedforward neural networks to approximate the actual error model and correct errors effectively.
Researchers used EEG to study consumer behavior and found that men are willing to pay more for premium chocolate, especially if it's expensive. The study also showed that packaging can influence willingness to pay, with well-known brands increasing prices by 9.9%.
A unique microcircuit in fruit flies' visual system transforms a single type of neuronal input to compute direction selectivity, with no inhibitory neurons present. The discovery reveals a striking example of the multilayered mechanisms of inhibition and excitation in the brain.
Researchers found that formalin fixation does not significantly alter the polarimetric properties of brain tissue, making it suitable for training machine-learning models. The study suggests that formalin-fixed brain tissue specimens can provide high-quality data for rapid and accurate diagnostic imaging in surgery.
Researchers have identified a novel model and therapy to mitigate the development of REM sleep behavior disorder, which affects over 3 million Americans. Dual orexin receptor antagonists have been shown to significantly reduce dream enactment behaviors, providing a promising new treatment option.
Researchers have developed a new method to study ant brain chemistry, revealing differences in neuropeptide distribution between two species. The approach integrates 3D chemical data with high-definition anatomical models, providing unbiased visualization of neurochemistry.
Researchers at UVA Health System have made a breakthrough discovery about the role of microglia in seizure disorders. The study suggests that enhancing microglial activity could be a promising approach to preventing and managing seizures, offering new hope for patients who don't respond to existing treatments.
A novel stroke model in pigs has been established to mimic human cerebral artery occlusion and study mechanisms of ischemic stroke. The model's translational features make it suitable for testing new therapeutic compounds and devices.
A machine learning model helps explain how brains recognize the meaning of communication sounds, such as animal calls or spoken words. The study models sound-processing networks in social animals' brains and demonstrates their ability to distinguish between different sound categories.
Researchers at Cedars-Sinai created computational models to bridge the gap between
Researchers developed a new framework for modeling task-switching, mimicking human behavior. The framework revealed two regions of the model's 'brain' doing each task, explaining the switch cost and potential benefits of splitting tasks.
A UTEP researcher has received a prestigious NSF grant to support his research on the neural mechanisms of decision-making. The award will also fund educational components and undergraduate/graduate courses focused on computational and biological science.
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.
A large-scale study found that diagnostic errors in neuroradiology were associated with longer interpretation times and higher shift volumes. The study also revealed a significant increase in diagnostic errors during weekend work, highlighting the need for targeted quality improvement interventions.