Articles tagged with Neural Simulation
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 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.
Researchers have developed a microscale optoelectronic tetherless electrode that can transmit brain activity data wirelessly for more than a year. Powered by red and infrared laser beams, the device transmits data back using tiny pulses of infrared light.
Scientists at Linköping University develop artificial neurons made of conductive plastics that perform advanced functions like biological nerve cells. They simplify the basic structure to make it compact and biologically relevant.
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.
Researchers developed a novel 2D phase-transition memristor leveraging intrinsic ion migration to overcome existing device limitations. The device achieves record-low power consumption, ultrafast switching speed, and exceptional endurance, making it suitable for high-speed computing applications.
Researchers at Nagoya University have discovered a unique sound stimulation technology that alleviates motion sickness, reducing symptoms by up to a minute of stimulation. The device stimulates the inner ear with a specific wavelength of sound, activating the vestibular system and improving balance.
Researchers have developed a computer simulation of brain neuron growth, using Approximate Bayesian Computation. The model successfully mimicked the growth patterns of real hippocampal neurons, showing promise for understanding neurodegenerative diseases and potential treatments.
Researchers at Duke University have developed a computer model that simulates nerve responses to electrical stimulation, enabling the efficient design of more effective and targeted neuromodulation therapies. The new tool, called S-MF, runs thousands of times faster than current industry standards without sacrificing accuracy or detail.
Researchers at Texas A🏠M University School of Engineering Medicine have developed an innovative project to refine the skills of NFL officials through neuro-ophthalmologic training. The project uses 2D game data to create 3D simulations, enhancing training and on-field performance.
A new study from the University of Washington suggests that cortical implants like Blindsight, developed by Neuralink, will not provide high-resolution vision. Instead, researchers found that a single electrode stimulates only one neuron, and image sharpness is determined by thousands of neurons in the brain.
Politecnico researchers developed a new type of neural network called Latent Dynamics Network (LDNet) that can accurately predict the evolution of complex systems in low-dimensional spaces. This approach offers significant innovations over traditional methods, enabling up to 5 times more accurate results with a reduction of over 90% in...
A recent study published in PLOS Biology found that the human brain can segregate direct speech from its echo, allowing for reliable recognition of echoic speech. This neural separation is essential for understanding conversations in noisy environments and is supported by magnetoencephalography recordings.
Researchers developed a deep convolutional neural network to pinpoint cardiac catheter tip locations in photoacoustic images, achieving high precision and recall. The approach has the potential to replace fluoroscopy during cardiac interventions, leading to safer procedures.
Australian researchers developed an algorithm that can intercept and shut down man-in-the-middle cyberattacks on unmanned military robots. The algorithm, tested on a replica of a US army combat ground vehicle, was 99% successful in preventing malicious attacks.
A Tohoku University research team developed a technique to recreate water flow in real-time using only a small amount of data from real water. This technology enables the creation of immersive VR games with lifelike water motion, potentially enhancing online communication and remote interactions.
HBP researchers develop a robotic platform capable of learning as humans do while navigating around a space. The simulated hippocampus alters its own synaptic connections to remember paths, a marked improvement over current methods relying on deep learning.
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.
Researchers developed a high-speed prediction model combining physical simulations and machine learning, achieving high accuracy without compromising computation time. The technology uses correspondence between input physical conditions and abstract data space handled by machine learning algorithms.
Scientists have identified a key molecular mechanism linking gene expression to synaptic plasticity in the aging human brain. The study reveals that age-related reductions in neurotransmitter receptor genes contribute to alterations in synaptic function, providing potential targets for Alzheimer's disease therapy.
The new Cell Atlas updates reveal that 20% of all neurons in the mouse brain are inhibitory, with detailed information on morphological and electrophysiological properties. The atlas combines various datasets to create a coherent framework for simulating neural circuits.
Researchers at RIKEN CBS demonstrate that neural networks minimize energy cost and solve mazes efficiently, pointing to a set of universal mathematical rules. The findings will aid in analyzing impaired brain function and generating optimized neural networks for artificial intelligences.
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.