Articles tagged with Computational Neuroscience
Scientists have developed a new technology to decode brain activity and infer the meaning of a person's mental picture. The study used electrocorticogram to detect patterns in brain activity while patients imagined different images, revealing distinct representations for each concept. This breakthrough could lead to the development of ...
Professor Alexander Ecker is awarded a Starting Grant to develop machine-learning methods to describe neurons' shape and function, leveraging a large dataset from the US Brain Initiative. The research aims to uncover how a neuron's shape relates to its role in sensory information processing.
Researchers have published an extensive 7T fMRI dataset to study how humans perceive and interpret naturalistic photographs. The Natural Scenes Dataset provides a massive scale of brain data for training complex deep-learning models that predict brain activity.
Carlos Ponce is studying the parts of the visual system that analyze shapes, using macaque monkeys as a model. He combines computational models with electrophysiology experiments to understand how neurons process visual information.
A study published in eLife found that rats exhibit efficient coding processes for visual stimuli, similar to those observed in humans. This suggests a universal principle in vision, where the brain adapts to its environment by specializing in the recognition of informative signals, thereby conserving computational resources and energy.
Astrocytes play a crucial role in self-repairing the brain and may hold the key to creating energy-efficient artificial intelligence. By emulating astrocyte functions in hardware devices, researchers aim to reduce power consumption and increase fault resilience.
A new study reveals that high-performing AI next-word prediction models resemble the function of language-processing centers in the human brain. The models' activity patterns closely match those seen in the brain during language tasks, suggesting a potential connection between AI and human language processing.
The Human Brain Project unveiled its 8th annual Summit with presentations of new scientific insights and technologies. The project has developed a powerful new infrastructure called EBRAINS, which will serve generations of brain scientists.
Researchers from Cold Spring Harbor Laboratory have published a comprehensive census of cell types in the primary motor cortex of human, mouse, and monkey brains. This milestone could lead to breakthroughs in studying and treating conditions like schizophrenia, depression, Alzheimer's, and traumatic brain injuries.
Researchers used fractal analysis to study brain network patterns while listening to a story. The results show that complex thoughts are reflected in high-order dynamic correlations in neural activity patterns.
Researchers at Osaka University used machine learning to analyze locomotion data from diverse species, revealing common features associated with dopamine deficiency. The study found that worms, mice, and humans exhibit similar movement disorders when lacking dopamine, despite their evolutionary differences.
A team of researchers found that when people use mental abstractions, the brain area that signals valuable information becomes highly active. This discovery could lead to new advances in basic research, education, rehabilitation, and artificial intelligence.
Emery N. Brown, a neuroscientist and practicing anesthesiologist, has won the SfN's Swartz Prize for his contributions to neural signal processing, anesthetic mechanisms, and statistical methods. His research advances neuroscientific understanding of how anesthetics affect the brain, improving patient care.
The Alfred P. Sloan Foundation has selected 126 U.S. and Canadian researchers as the recipients of the 2019 Sloan Research Fellowships. These fellowships recognize early-career scholars whose achievements mark them as among the most promising researchers in their fields.
The study combines synfire communication, coherence and resonance to provide insight into how messages are exchanged between brain areas. The researchers found that oscillations play a significant role in determining whether communication can take place.
Kenneth Miller received the 2018 Swartz Prize in Theoretical and Computational Neuroscience for his contributions to modeling cerebral cortex function. His research has led to a reliable model of synaptic plasticity, confirmed by experimental data.
Researchers developed high-speed computational software 'Parallel STEPS' to model neuronal interactions and functions. The new approach achieved significantly improved performance, enabling faster simulation of complex models and revealing new insights into individual neuron behavior.
Researchers propose a mathematical theory explaining grid cell activity, enabling precise representation of spatial position and direction. The framework combines population-vector decoding with grid scale progression to maximize spatial resolution.
Terrence Sejnowski receives the Swartz Prize for his significant cumulative contribution to theoretical models and computational methods in neuroscience. He has made major research discoveries, including learning models for birdsong and neuroeconomics.
A new study by neuroscientists at the Bernstein Center Munich found that previous experience plays a significant role in shaping our perceptions of physical quantities. The researchers developed a Bayesian model that integrates two classic theories and explains how prior knowledge affects judgment, leading to systematic estimation biases.
Researchers have linked specific neurons to a type of somatosensation, opening the door for a better understanding of our sense of touch. The study found that these neurons respond uniquely to certain tactile stimuli and receive direct synaptic input from other brain regions.
Researchers have developed a new method to analyze electrical activity in the brain, enabling better diagnosis and treatment of brain illnesses. The technique can help identify impending seizures and paralysis, allowing for more effective interventions.
Scientists at LMU Munich developed a mathematical model that accurately mimics human sound recognition, showing the brain compares stored and perceived sounds efficiently. The new library-based model predicts the flow of information from cerebrum to thalamus, enabling highly efficient real-time implementation.
A walking robot, RunBot, has been engineered to mimic human gait and adapt to different environments, including mountainous terrain. The robot's ability to learn from trial and error allows it to adjust its gait in response to visual input, ensuring stability and balance on slopes.
Researchers developed a new approach to interpreting brain electroencephalograms that provides an unprecedented view of thought in action. The technique uses Independent Component Analysis to separate signals from different areas of the brain, enabling detailed insights into neural dynamics.
A Princeton scientist created a computer model of a mouse brain that can recognize spoken words, challenging conventional views on sensory perception. The model uses sensitive timing of electrical signals to perform calculations, potentially leading to faster and more sophisticated brain functions.