Articles tagged with Neural Modeling
Researchers propose a new theory explaining why humans perceive colors as combinations of red, yellow, green, and blue. They suggest that our brain simplifies the natural world's color palette to encode the range of colors, capturing the idea of opponency in color vision.
Lehigh University researchers develop comprehensive mathematical model of the neural circuitry linking the gut and brain to regulate stomach function, opening new pathways for treating chronic digestive disorders through targeted vagus nerve stimulation. The digital twin could help accelerate therapy development and improve quality of ...
The new section in JMIR Neurotechnology focuses on the responsible and ethical use of neurotechnology. Submissions welcome for original research, case series, theoretical viewpoints, and data-driven analyses on ethics and policy in neurotechnology.
A research team has pioneered a novel optical neural network that physically 'learns' through associative light exposure, eliminating the need for traditional computing algorithms. The system successfully recognized letters and demonstrated capability in handwritten digit recognition.
Researchers have successfully reconstructed videos from mouse brain activity, allowing them to understand how the brain processes visual information. By analyzing the neural activity in the visual cortex, the team created high-quality reconstructions of 10-second video clips, revealing the brain's representation of visual cues.
Researchers suggest that cells descend from the same progenitor tend to remain near one another, forming a lineage-based mechanism to convey positional information. This theory could have far-reaching implications for biology and artificial intelligence, potentially solving fundamental mysteries of the mind.
Researchers have developed a smaller and simpler AI model that accurately predicts neural responses to visual stimuli in macaque brains. The compact model reveals unique neuron preferences for features like edges and colors, shedding light on how the brain processes information.
Researchers used machine learning techniques to compress a large model of the visual cortex, creating smaller versions that predict neural responses with high accuracy. The compact models revealed specific computational patterns in how neurons detect important features, offering insights into how visual information is processed.
Researchers at UC Santa Cruz successfully train brain organoids to balance a virtual pole, demonstrating adaptive learning capabilities. This breakthrough has implications for understanding neurological conditions and developing new tools for studying brain function.
Scientists at Max Planck Florida Institute for Neuroscience have identified a neural code that allows the brain to track distance traveled without visual cues. This ability is essential for navigation and memory formation, and may offer insight into early navigation problems in Alzheimer's disease.
Researchers propose that neural activity in each animal becomes linked through perception, allowing individuals to maintain bearings towards others relative to stable features in the environment. This synchronization gives rise to spontaneous alignment and collective movement.
A dual-source data-driven gated spatiotemporal fusion network significantly enhances the accuracy of fine-scale lightning forecasting. The model combines the strengths of weather foundation models and recent lightning observations, leveraging complementary features to improve forecast performance.
Professor John O'Brien's research focuses on understanding the impact of electrical synapses and gap junction plasticity on the retina and other neurological functions. The study aims to identify proteins that control electrical synapse strength, shedding light on their role in human disorders such as autism and seizures.
Researchers found that brain's dopamine neurons encode a map of possible future rewards across time and magnitude, guiding adaptive behavior in uncertain environments. This biological insight aligns with recent advances in AI, particularly distributional RL algorithms, which learn from reward distributions rather than averages.
Researchers have used AI and brain activity to study the Other-Race-Effect (ORE), a phenomenon where people recognize faces of their own race more easily than others. They found that faces from other races are processed more generally and with less detail, leading to less accurate recognition.
Researchers from the University of Texas at San Antonio discovered a significant link between REM sleep disturbances and increased PTSD severity. The study suggests that targeting REM sleep could be a promising approach for improving PTSD treatment outcomes.
A study published in Communications Psychology reveals how the brain forms a sense of agency when learning new motor skills. Researchers used a data glove to control a cursor on screen, finding that motor exploration plays a crucial role in developing self-agency.
The framework utilizes consortium blockchain architecture for immutable records, minimizing data falsification and cyberattacks. AI-driven tools ensure objective data collection and analysis, reducing human bias.
Researchers have developed a neuro-quantum leap in finding optimal solutions, leveraging Fowler-Nordheim annealers to discover new and unknown solutions. NeuroSA's structure is neuromorphic, with a search behavior determined by FN annealer, making it powerful for solving complex optimization problems.
A University of Ottawa-led study reveals that serotonin neurons are connected and interact with each other, controlling serotonin release in specific regions of the brain. This complex system has implications for understanding decision-making and developing targeted therapeutics for mood disorders.
A new study reveals promising progress toward predicting how patients with major depressive disorder will respond to antidepressant medications. Brain connectivity patterns were found to significantly improve predictions of treatment response across two large, independent clinical trials.
Researchers analyzed massive audio recordings to create a dictionary of short melodies in English-language prosody, assigning functions and meanings. They discovered hundreds of basic patterns with linguistic functions, including conveying attitudes such as curiosity or surprise, and identified syntactic rules governing their order.
Researchers at Texas Tech University Health Sciences Center have received a $1.94 million grant to study inhibitors that target peripheral neuropathic pain. The project aims to develop novel non-opioid and non-addicting therapies capable of effectively managing chronic pain.
A team of researchers developed Lp-Convolution, a novel method that uses multivariate p-generalized normal distribution to reshape CNN filters dynamically. This breakthrough improves the accuracy and efficiency of image recognition systems while reducing computational burden.
A study published in PLOS Biology found that individuals who receive dynamic facial-expression feedback from an avatar's face tend to gamble more than those receiving real human feedback. The amygdala plays a key role in this facilitation, with increased valuation of uncertainty contributing to increased risk-taking behavior.
Researchers investigated how brains process language during real-life conversations, employing AI to analyze neural activity and linguistic features. They found that speaking and listening engage widespread networks of brain areas in the frontal and temporal lobes, with highly specific patterns changing depending on words and context.
MIT researchers have found that a computational model of the ventral stream, which processes object recognition, also performs well on spatial tasks such as determining an object's location and orientation. This challenges the dominant perspective that the ventral stream is optimized for object recognition.
A novel channel-wise cumulative spike train image-driven model (cwCST-CNN) is presented for hand gesture recognition, achieving a classification accuracy of 96.92% in recognizing 10 gestures. The method leverages HD-sEMG signals and reconstructs them into two-dimensional images to capture spatial activation patterns.
A new hardware platform for AI accelerators capable of handling significant workloads with reduced energy requirement has been developed. The platform leverages III-V compound semiconductors to create photonic integrated circuits, which operate at the speed of light with minimal energy loss.
Scientists have built a digital twin of the mouse brain's visual cortex using AI, predicting neural activity and anatomical features. The model can generalize to new visual inputs and data, speeding up brain research and understanding intelligence.
Researchers investigated the neuroregenerative potential of 5-NOT and Epi, discovering they protected SH-SY5Y cells from MPP+ toxicity by regulating key proteins. The findings support 5-NOT as a glycomimetic drug candidate for Parkinson's disease treatment.
Researchers created a promising approach that augments LLMs with graph-based models to generate molecules with desired properties. The method generates molecules with better matching user specifications and valid synthesis plans, outperforming existing LLM-based approaches.
A University of Ottawa-led research team has deciphered the message that serotonin conveys to the brain, discovering a 'prospective code for value' that explains why neurons are activated by rewards and punishments. This finding has implications across multiple fields, including neuroscience, psychology, and psychiatry.
Researchers at Concordia University have developed a new approach to identifying fake news on social media using the SmoothDetector model. The model integrates probabilistic algorithms with deep neural networks to capture uncertainties and patterns in multimodal data, providing more nuanced judgments of authenticity.
A new study published in Brain Medicine found that administering Osanetant shortly after a traumatic event significantly dampens fear expression in female mice. This suggests that targeting fear memory at its roots may be an effective intervention to reduce PTSD-like behaviors.
A new study has revealed that Beagle dogs carrying mutations in the Shank3 gene exhibit face processing abnormalities, similar to those observed in human ASD patients. The research provides direct experimental evidence that mutations in Shank3 lead to ASD-like deficits in face processing, contributing to social impairments.
A new simulation of brain metabolism has revealed potential targets for future dementia treatments, according to a study published in Frontiers in Science. The model, available on the Open Brain Platform, could help scientists accelerate research into interventions promoting healthy brain aging.
Researchers developed a machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. The new surrogate model maintains the same level of accuracy as traditional particle-based simulations while reducing computation time from approximately 45 minutes to just three minutes.
Scientists at ISTA create a new brain organoid model that incorporates microglia to study viral infections, such as Rubella, and test the effectiveness of ibuprofen. The results show that microglia play a crucial role in inflammatory reactions and that ibuprofen exerts its protective effects by inhibiting two inflammatory enzymes.
Researchers from UC Berkeley and UCSF developed a streaming method that synthesizes brain signals into audible speech in near-real time, solving the latency challenge. The technology represents a critical step toward enabling communication for people who have lost the ability to speak, with high decoding accuracy and naturalistic fluency.
Researchers at HKUST established a pre-OPN OTI model to investigate functional axonal rewiring following CNS injury. They found that intrinsically photosensitive RGCs mediate functional recovery, and proposed a dual-intervention strategy to enhance regeneration efficiency
Researchers at Weill Cornell Medicine developed a new AI model that harnesses whole-slide tumor imaging data and gene expression analyses to predict how patients with muscle-invasive bladder cancer will respond to chemotherapy. The model outperforms previous models using a single data type, identifying key genes and tumor characteristi...
Researchers develop AI model to predict novel mutations in protein sequences, combining grammatical and semantic changes. The method uses all available information about the sequence and mutations to create a more accurate prediction model.
Researchers created a memristor-based brain-computer interface decoding system that achieves high accuracy while consuming significantly less energy than traditional systems. The system demonstrated impressive capabilities in real-world testing, including improved stability and accuracy through co-evolution with brain signals.
A new study presents a proof-of-concept leptomeningeal neural organoid (LMNO) fusion model to study meninges-brain signaling. The co-culture system of neural organoids fused with fetal leptomeninges from mice demonstrates stability and interface characteristics.
A new open-source model of brain metabolism has shown how altering key chemicals can restore aged cells to their youthful activity and resilience. The detailed simulation provides a glimpse into the impact of aging on brain metabolism and highlights potential drug targets.
The study systematically traces Generative AI evolution from deep learning to foundation models, highlighting four distinct stages and successful applications. Key challenges like safety concerns and theoretical breakthroughs require further attention and development in the field of Generative AI.
Crew-9 astronauts conducted research on protein crystallization studies and a new drug for Alzheimer's, dementia, and Parkinson's. They also supported student-led experiments, contributing to the next generation of explorers.
Researchers from Virginia Tech have published a visionary paper on fusing wireless technologies and AI to create human-like common sense. The team aims to develop a network that can think, plan, and imagine like humans, enabling seamless merging of physical, virtual, and digital dimensions.
Researchers have mapped the long-range synaptic connections involved in vocal learning in zebra finches, uncovering new details about how the brain organises learned vocalisations. The study provides a framework for understanding how the brain integrates sensory and motor information to guide learned vocal behaviour.
Researchers at Saarland University are developing leaner, customized AI models and techniques like knowledge distillation to reduce energy consumption. These smaller models enable small and medium-sized businesses to access powerful AI technology without a large technical infrastructure.
Researchers identified 11 cuproptosis-related genes and classified IDH1-mutant gliomas into two subtypes with distinct clinical outcomes. A risk model using FDX1/SLC31A1-based features demonstrated superior prognostic performance, providing insights for chemotherapy selection and treatment resistance.
Distributed acoustic sensing systems face data processing speed limitations; researchers leverage photonic neural networks to overcome these challenges. The TWM-PNNA system achieves high recognition accuracy above 90% with low power consumption, outperforming electrical GPUs by orders of magnitude.
A new study by UCLA Health has discovered a drug that fully reproduces the effects of physical stroke rehabilitation in model mice. The drug, DDL-920, excites parvalbumin neurons, leading to significant recovery in movement control after stroke.
Researchers at Baylor College of Medicine have identified new variants of CDKL2 and CDKL1 genes associated with developmental disorders, including epilepsy. The study proposes a mechanism by which defective variants cause neurological symptoms in affected individuals.
A new study challenges the long-held belief that fear is primarily communicated through facial expressions, suggesting instead that situational context plays a critical role in fear recognition. The research involved analyzing real-life fear reactions in videos and found that facial expressions alone fail to reliably signal fear.
A Virginia Tech study reveals that machine learning models are inadequate in detecting critical patient conditions, missing 66% of injuries. The research emphasizes the need for more responsive models incorporating medical knowledge to improve healthcare outcomes.
Researchers used advanced technology to analyze over 100 hours of brain activity during real-life discussions. The study found that the brain processes language in a sequence, from thinking about words to forming sounds and vice versa.
A new brain-mapping technique identified memory-related brain cells vulnerable to protein buildup, a key factor in Alzheimer's disease. The study found that certain cell types in the hippocampus and cortex were more affected by tau buildup.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.