Articles tagged with Computational Neuroscience
Researchers analyzed EBRAINS-hosted brain data to determine that whole-brain activity patterns predict stimulation outcomes better than local recordings. Targeting specific sensorimotor and visual networks reduces response variability by up to 24%. This study highlights the value of open datasets for enabling large-scale analyses.
Researchers developed a computer model of the human cortex linking microscopic chemistry to brain-wide patterns of activity. The model found that regional differences in receptor density improve coordination between brain regions and information flow.
Researchers at Japan Advanced Institute of Science and Technology developed a new computational method combining neural networks with Bayesian localization, achieving accurate predictions while reducing computational cost. This breakthrough enables the high-precision analysis of large-scale materials and complex chemical reaction systems.
Researchers at Columbia's Zuckerman Institute found that specialist neurons exist but are the exception, not the rule. Most neurons can display a huge diversity of responses and help the brain solve complex tasks.
A new study finds that human cortical neurons have remarkable computational capabilities, surpassing those of other mammals. The researchers developed a new method to measure the complexity of individual neurons, revealing their sophisticated computing power.
Researchers developed an electronic device that combines processing and memory into a single component, enabling analog operation with multiple intermediate states. The device exhibits synaptic plasticity, reconfigurable logic, and low energy consumption, showcasing its potential for neuromorphic computing advancements.
Researchers found that human brains predict word sequences similar to AI language models' processes, suggesting a shared information processing principle. The study's results corroborate key assumptions in cognitive neuroscience, shedding light on the effectiveness of AI language models in various applications.
A study published in Scientific Reports found that programmers' brains exhibit brain activity similar to those caused by unexpected turns in conversation when encountering confusing code snippets. The research team used EEG and eye-tracking data to analyze the brain patterns, revealing a striking pattern known as late frontal positivity.
A study by Brown University researchers found that serotonin helps reduce belief stickiness, making it easier to update beliefs and adapt to changes. This discovery holds implications for understanding and treating obsessive-compulsive disorder (OCD).
A new study uses real-time fMRI neurofeedback to train people to regulate rumination, a common symptom of depression, by restoring healthy neural coupling between the posterior cingulate cortex and dorsolateral prefrontal cortex. The approach shows promise in reducing depressive symptoms while leaving anxiety intact.
The researchers' hybrid system combines neuromorphic-inspired auto encoding and Fowler-Nordheim annealing to find the most efficient solution. This approach differs from classical computing methods and offers convergence guarantees, ensuring a solution will be found within a set timeframe.
Researchers at Columbia University's Zuckerman Institute have developed a brain-controlled hearing system that can help people focus on one conversation among many. The system, which leverages the brain's natural ability to filter through background noise, dynamically isolates specific conversations in real-time.
University of Missouri researchers develop organic transistors that process information like biological neural networks, boosting brain-like computing and potentially leading to more energy-efficient artificial intelligence. The approach could lead to significant improvements in tasks such as pattern recognition and decision-making.
The EBRAINS Roadmap Symposium will bring together the global neuroscience community to shape the EBRAINS 10-Year-Roadmap. A total of 159 submissions from 134 unique contributors across 25 countries have been received, reflecting the momentum of Europe's digital neuroscience landscape.
Terrence Sejnowski receives Scientific Breakthrough Award for his foundational development of Boltzmann machines, providing the architectural bedrock for deep learning and generative AI. His work has had a profound impact on modern artificial intelligence and tools like ChatGPT.
Researchers propose a novel brain architecture for efficient processing, integrating parallel cortical and subcortical pathways. This approach may improve decision-making tasks, suggesting current AI models are missing key brain function principles.
A new model, Xi–αNET, explains how brain structure and signal conduction speed shape alpha waves and background activity. The study found that faster conduction speeds in younger individuals correspond to higher alpha frequencies, while slower conduction speeds in older adults lead to declining alpha frequencies.
A new study reveals that the hippocampus represents emotion concepts in a structured hierarchy of pleasantness and bodily reaction, while the ventromedial prefrontal cortex tracks relationships between these nodes. This map-like representation may help in the treatment of mental illnesses, such as depression and anxiety.
Donoghue was recognized for his foundational leadership in advancing brain–computer interfaces, enabling individuals with paralysis to regain independence. His work has shown that neural activity persists even after spinal cord injury or neurological illnesses like ALS have robbed people of their ability to move.
Researchers have engineered a protein that can detect the faintest incoming chemical signals of brain cells, allowing for real-time decoding of neural activity. This breakthrough enables scientists to understand the complex cascade of electrical activity underlying learning, memory, and emotion.
Researchers at Ritsumeikan University have discovered that the loss of TRPM1 ion channels sets off a cascade of changes leading to persistent oscillations in the retina. This finding illuminates the cellular basis of congenital stationary night blindness and identifies a common mechanism underlying retinal degenerative conditions.
Researchers developed a novel topology-aware multiscale feature fusion network to enhance EEG-based motor imagery decoding. The TA-MFF network achieves excellent classification performance, outperforming state-of-the-art methods by leveraging spectral-topological data analysis-processing and inter-spectral recursive attention.
The superior colliculus, an ancestral brain structure, enables the brain to distinguish objects from the background and detect relevant stimuli in space. It generates centre–surround interactions independently, allowing for the detection of contrasts, edges, and salient features.
Researchers at Virginia Tech found that adjusting molecular processes can improve memory in older subjects. They used CRISPR-dCas13 and CRISPR-dCas9 to target age-related changes in K63 polyubiquitination and IGF2, two genes linked to memory formation.
Terrence Sejnowski, a renowned neuroscientist at Salk Institute, has been awarded the 2025 NIH Director's Pioneer Award to develop advanced computational techniques for analyzing working memory. His research aims to better understand and treat memory disorders in mental health conditions.
Researchers used precise neural activity measurements from epilepsy patients to study how brain processes speech. The findings suggest the auditory cortex operates on a fixed, internal timescale independent of speech structures, providing a consistently timed stream of information.
A new study in Nature Communications found that AI models exhibit a geometric property called convexity, which helps humans form and share concepts. Convexity is also linked to the performance of AI models on specific tasks.
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.
A recent study found that caffeine increases brain signal complexity and enhances criticality during sleep, with effects more pronounced in young adults. This can lead to a state where the brain is neither fully awake nor relaxed, potentially interfering with restful recovery.
Researchers have successfully modeled the synaptic vesicle cycle with unprecedented detail, shedding new light on how our brains function. The model predicts parameters of synaptic function that could not be tested experimentally, opening new avenues for neuroscience investigations.
Researchers have developed CaliAli, an advanced analytical framework that aligns calcium imaging data across multiple sessions, allowing for the continuous tracking of individual neurons. This breakthrough enables long-term brain activity studies and advances understanding of memory formation, retention, and neurological diseases.
Scientists at MIT have identified new potential targets for treating Alzheimer's disease, including a pathway involved in DNA damage repair. The study suggests that a combination of treatments targeting different cellular pathways may be more effective in blocking disease progression.
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 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.
Researchers have identified differences in neural connectivity patterns between patients with recovered and persistent psychosis. These differences enable the development of new intervention strategies for patients with psychosis, according to a study published in Nature Mental Health.
The research aims to bridge the translational gap between animal and human studies, exploring how obesity impacts decision-making around food. The team will measure brain chemical activity while participants consume sugary drinks and complete emotion-related tasks.
A novel brain study uncovers the critical role of the HDAC5 enzyme in regulating gene expression and neuronal activity, which can trigger relapse in individuals with substance use disorders. The study highlights a new molecular target for developing novel treatments to reduce relapse risk.
Researchers developed a freely available analysis tool, DANCE, for automating the quantification of male aggression and courtship behaviors in fruit flies. The tool uses machine learning and has been shown to be as accurate as expert manual scoring with costs less than $0.30 per experiment.
A new review article identifies major themes in AI application to neurosciences, including a 5-fold increase in AI-related publications. The study also notes a surge of over 13-fold in clinical neurology AI-related publications in the past decade.
The Open Brain Institute launches a groundbreaking platform to simulate and study digital brains, empowering researchers to explore brain complexity and diseases. With its virtual neuroscience laboratories, the OBI enables global collaboration and access to cutting-edge virtual labs.
A new model of brain computational analysis, called CHARM, reveals that the human brain can surpass machines in critical decision-making. The CHARM model applies quantum mechanics to analyze long-distance brain connections, enabling precise modeling of the brain's dynamics.
A new model developed by researchers at the University of Barcelona accurately predicts how elite athletes move to catch a moving object just from an initial glance. The model integrates prior knowledge of gravity and physical size into visual information, allowing for precise predictions of trajectory and time until impact.
The study uses PCArs to analyze brain neuronal activity in various situations, revealing that different brain regions exhibit distinct geometric figures during object recognition and memory tasks. The findings suggest that the brain processes visual information in real-time and changes its activity pattern from moment to moment.
Researchers found neurotransmitters in the human brain are released during emotional language processing, providing insights into how words shape our choices and mental health. The study bridges biology and symbolism, linking neural processes to human communication and emotion.
The study reveals that directional connections propagate signals in a downstream flow, leading to more complex activity patterns. Mathematical models also suggest that modularity and connectivity interact to foster dynamical complexity.
Researchers at KAIST developed a new method to learn without weight transport, enabling faster and more accurate learning. By pre-training with random noise, the team showed that neural networks can achieve high learning efficiency and solve the weight transport problem.
Marco Celotto and Simone Russo were awarded the Lagomarsino Prize for their groundbreaking research in neuroscience, which combined excellent scientific results with outstanding methods for data analysis, data management, and Open Science. Their work has led to pioneering discoveries on brain stimulation and its potential applications ...
Researchers discovered that NMDA receptors set the baseline level for neural network activity, helping maintain stable brain function. The study's findings suggest potential innovative treatments for diseases linked to disrupted neural stability.
Researchers identified hundreds of brain proteins associated with inter-individual differences in functional connectivity and structural covariation. The proteins were enriched for those involved in synapses, energy metabolism, and RNA processing, providing insights into the mechanistic basis of human cognition and behavior.
A research group at the University of Tsukuba discovered that the dorsal premotor cortex plays a crucial role in meta-learning for motor skills. The study found that stimulating this region impairs the meta-learning effect on memory forgetting, but not motor learning itself.
A recent study published in Nature Cancer has discovered that cancer cells exhibit opposing pro and antitumor programs, with the former promoting metastasis and the latter combating tumor growth. This finding opens new avenues for therapeutic strategies to target highly aggressive and therapy-resistant tumors.
Studies show that the cerebellum is critical for forming stable memories for sensorimotor skills, largely independent of short-term memory systems. Researchers found that longer time intervals between trials increased reliance on impaired long-term memory.
Researchers have discovered that small networks of neurons in the fruit fly's brain can generate an accurate internal compass, contrary to previous assumptions. This finding expands our knowledge of what small networks can do and challenges traditional views on brain size and function.
A new study reveals that a third of glioma cells, a type of brain tumor, fire electrical impulses. These hybrid cells combine features of neurons and glia, challenging the long-held notion that only neurons generate electric signals in the brain.
Researchers at WVU created a motion-compatible brain scanner that allows patients to move around during imaging. The Ambulatory Motion-enabling PET (AMPET) scanner can help study human behaviors, balance, and emotions, and may be used to monitor brain activity for PTSD treatment and mindfulness meditation.
Researchers developed a motor meta-learning task to assess stroke patients' ability to recognize their learning capabilities and plan practice. The study found a significant correlation between meta-learning ability and improvement index, suggesting that enhanced individual meta-learning ability can improve rehabilitation efficacy.
Researchers have identified a universal blueprint for mammalian brain shape, describing the cerebral cortex as following a fractal pattern across species. The study suggests that cortices across primate species resemble this universal scaling law and self-similarity, revealing a common set of mechanisms governing cortical folding.
Researchers found that small regions of the brain can momentarily 'flicker' awake while the rest of the brain remains asleep, and vice versa from wake to sleep. This challenges traditional understanding of sleep and wake patterns, which have been defined by slow, long-lasting waves.
Researchers discovered that propofol, a commonly used anesthesia drug, induces unconsciousness by causing the brain to become increasingly unstable. This instability leads to chaotic brain activity, resulting in loss of consciousness. The study's findings could help develop better tools for monitoring patients during general anesthesia.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.