Neuroscience research articles are provided.
What is neuroscience? Neuroscience is the scientific study of nervous systems. Neuroscience can involve research from many branches of science including those involving neurology, brain science, neurobiology, psychology, computer science, artificial intelligence, statistics, prosthetics, neuroimaging, engineering, medicine, physics, mathematics, pharmacology, electrophysiology, biology, robotics and technology.
– These articles focus mainly on neurology research. – What is neurology? – Definition of neurology: a science involved in the study of the nervous systems, especially of the diseases and disorders affecting them. – Neurology research can include information involving brain research, neurological disorders, medicine, brain cancer, peripheral nervous systems, central nervous systems, nerve damage, brain tumors, seizures, neurosurgery, electrophysiology, BMI, brain injuries, paralysis and spinal cord treatments.
What is Psychology? Definition of Psychology: Psychology is the study of behavior in an individual, or group. Psychology news articles are listed below.
Artificial Intelligence articles involve programming, neural engineering, artificial neural networks, artificial life, a-life, floyds, boids, emergence, machine learning, neuralbots, neuralrobotics, computational neuroscience and more involving A.I. research.
Robotics articles will cover robotics research press releases. Robotics news from universities, labs, researchers, engineers, students, high schools, conventions, competitions and more are posted and welcome.
Genetics articles related to neuroscience research will be listed here.
Neurotechnology research articles deal with robotics, AI, deep learning, machine learning, Brain Computer Interfaces, neuroprosthetics, neural implants and more. Read the latest neurotech news articles below.
Findings, published in Nature, could lead to improved treatments for stroke, other brain injuries.
Learning a new skill is easier when it is related to an ability we already have. For example, a trained pianist can learn a new melody easier than learning how to hit a tennis serve.
Neural engineers from the Center for the Neural Basis of Cognition (CNBC)—a joint program between the University of Pittsburgh and Carnegie Mellon University—have discovered a fundamental constraint in the brain that may explain why this happens. Published as the cover story in the Aug. 28, 2014, issue of Nature, they found for the first time that there are constraints on how adaptable the brain is during learning and that these constraints are the key determinant for whether a new skill will be easy or difficult to learn. Understanding the ways in which the brain’s activity can be “flexed” during learning could eventually be used to develop better treatments for stroke and other brain injuries.
Lead author Patrick T. Sadtler, a PhD candidate in Pitt’s Department of Bioengineering, compared the study’s findings to cooking.
“Suppose you have flour, sugar, baking soda, eggs, salt, and milk. You can combine them to make different items—bread, pancakes, and cookies—but it would be difficult to make hamburger patties with the existing ingredients,” Sadtler said. “We found that the brain works in a similar way during learning. We found that subjects were able to more readily recombine familiar activity patterns in new ways relative to creating entirely novel patterns.”
For the study, the research team trained animals to use a brain-computer interface (BCI), similar to ones that have shown recent promise in clinical trials for assisting tetraplegics and amputees.
“This evolving technology is a powerful tool for brain research,” said Daofen Chen, program director at the National Institute of Neurological Disorders and Stroke (NINDS), part of the National Institutes of Health (NIH), which supported this research. “It helps scientists study the dynamics of brain circuits that may explain the neural basis of learning.”
The researchers recorded neural activity in the motor cortex and directed the recordings into a computer, which translated the activity into movement of a cursor on the computer screen. This technique allowed the team to specify the activity patterns that would move the cursor. The subjects’ goal was to move the cursor to targets on the screen, which required them to generate the patterns of neural activity that the experimenters had requested. If the subjects could move the cursor well, that meant that they had learned to generate the neural activity pattern that the researchers had specified.
The researchers found that their subjects learned to generate some neural activity patterns more easily than others, since they only sometimes achieved accurate cursor movements. The harder-to-learn patterns were different from any of the pre-existing patterns, whereas the easier-to-learn patterns were combinations of pre-existing brain patterns. Because the existing brain patterns likely reflect how the neurons are interconnected, the results suggest that the connectivity among neurons shapes learning.
Flexing the Brain: Carnegie Mellon, Pitt Scientists Discover Why Learning Tasks Can Be Difficult
“We wanted to study how the brain changes its activity when you learn and also how its activity cannot change. Cognitive flexibility has a limit—and we wanted to find out what that limit looks like in terms of neurons,” said Aaron P. Batista, assistant professor of bioengineering at Pitt and co-principal investigator on the study with Byron M. Yu, assistant professor of electrical and computer engineering and biomedical engineering at Carnegie Mellon. Yu believes this work demonstrates the utility of BCI for basic scientific studies that will eventually impact people’s lives.
“These findings could be the basis for novel rehabilitation procedures for the many neural disorders that are characterized by improper neural activity,” Yu said. “Restoring function might require a person to generate a new pattern of neural activity. We could use techniques similar to what were used in this study to coach patients to generate proper neural activity.”
[divider]Notes about this neuroscience and learning research[/divider]
The research was funded by the National Institutes of Health, the National Science Foundation, and the Burroughs Wellcome Fund.
In addition to Sadtler, Batista, and Yu, the research team included Pitt’s Kristin Quick and Elizabeth Tyler-Kabara, CMU’s Matthew Golub and Steven Chase, and Stephen Ryu of Stanford University and the Palo Alto Medical Foundation.
Contact: Dr Sonia Corrêa – University of Pittsburgh Source: University of Pittsburgh press release Image Source: The image is credited to Batista lab, University of Pittsburgh and is adapted from the press release Video Source: The video “Flexing the Brain: Carnegie Mellon, Pitt Scientists Discover Why Learning Tasks Can Be Difficult” is available at the CMUHSS YouTube page Original Research: Abstract for “Neural constraints on learning” by Patrick T. Sadtler, Kristin M. Quick, Matthew D. Golub, Steven M. Chase, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Byron M. Yu and Aaron P. Batista in Nature. Published online August 27 2014 doi:10.1038/nature13665
[divider]Share this Neuroscience Research News[/divider]