3d brain app7/5/2023 ![]() ![]() ![]() "Only a few months into the BrainHQ exercises the difference is clear, this isn’t left to speculation, I saw drastic improvements…and my family has noticed a strong improvement." - A.BrainHQ is fun and gives me the challenges I need to keep my brain working out on a daily basis." - C. "BrainHQ is a no-brainer must-have in my book…This training has expanded my visual, fine-tuned my audio and even my reflexes seem to be getting faster.physical brain changes, like healthier white matter and more coordinated neurons.Īll of this means that BrainHQ works-in laboratory studies, and in the lives of everyday people:.real-world improvements, including safer driving and improved quality of life. ![]() cognitive improvements, such as better memory and faster processing speed.More than 100 publications from independent, randomized controlled trials have shown that the exercises and assessments in BrainHQ drive real change. No other brain fitness program can match BrainHQ in scientific proof. The result: a training system that comprehensively improves brain performance, from the most basic elements of perception through the most complex elements of memory, attention, and cognitive function. Top scientists from around the world helped to develop and test the BrainHQ exercises. Now with new features that contribute to your brain fitness success!īrainHQ represents the culmination of 30 years of research in neuroscience and brain health. Learn more about the BigNeuron project on the Allen Institute for Brain Science’s website.Train your brain with BrainHQ from Posit Science-the most rigorous program available for better brain health, and the only one backed by more than 100 scientific papers showing benefits - including the breakthrough NIH-funded ACTIVE study. To mitigate this problem, the team developed an automated algorithm using deep learning to figure out the shape of each neuron inside a particular image. These factors make it difficult for existing algorithms to generalize effectively when they’re applied to volumes of images obtained by different labs. Recreating them has remained a challenge due to the diversity of species, brain location, developmental stages and quality of the microscopy image sets. Using high-resolution microscopes to capture 3D pictures of individual neurons, scientists have worked on developing fully automated neuron reconstruction methods for nearly 40 years. To understand how the brain functions and changes over time, scientists must be able to digitally reconstruct these neuronal structures to figure out the shape of each neuron in an image. In the human brain alone, there are hundreds of billions of neurons, and they are connected to each other via thousands of thin “branches,” forming a 3D treelike structure. The project will result in a large set of publicly available neural reconstruction data images, along with robust tools and algorithms researchers can use for their own analysis work. Then it will “bench test” the algorithms on large-scale datasets of images using supercomputers. Its goal is to develop a standard framework to help researchers define the best methods and algorithms for fast and accurate automatic neuron reconstruction. Initiated in 2015 and led by the Allen Institute for Brain Science, BigNeuron is an international initiative that brings together computer scientists and neuroscientists from a dozen institutions. Shuiwang Ji, a professor in the Department of Computer Science and Engineering at Texas A&M University, is part of a collaborative research community that recently had a paper published in the April issue of the journal Nature Methods. ![]()
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