WASHINGTON — Using machine learning, researchers are able to use data from the brain to glean deeper insights and apply this new knowledge in clinical settings. The findings will be presented on Monday, November 13, 2–3 p.m. EST at Neuroscience 2023 , the annual meeting of the Society for Neuroscience and the world’s largest source of emerging news about brain science and health.
Machine learning is a branch of artificial intelligence (AI) that centers on enabling computers to analyze data in increasingly complex ways. Researchers use algorithms with adaptive models that can change and develop without outside intervention. In neuroscience research, machine learning can study large datasets with information from the human brain and also apply models to predict outcomes based on that information.
New findings show that:
“Advances in AI and machine learning are transforming brain research and clinical treatments,” said Terry Sejnowski, the Francis Crick Professor at the Salk Institute for Biological Studies and distinguished professor at UC San Diego, who will moderate the press conference. “Brain recordings produce huge datasets that can be analyzed with machine learning. Predictive modeling, machine-brain interfaces, and neuroimaging/neuromodulation are areas with particular promise in developing new therapeutics and treatment plans for patients.”
This research was supported by national funding agencies including the National Institutes of Health and private funding organizations. Find out more about AI and brain research on BrainFacts.org .
Monday, November 13, 2023
2–3 p.m. EST
Walter E. Washington Convention Center, Room 202B
Press Conference Summary
Unsupervised-based feature selection method robustly extracted resting state functional connectivity related to major depressive disorder
Ayumu Yamashita, ayumu722@gmail.com , Abstract PSTR099.15
Predicting future decline from mild cognitive impairment to Alzheimer’s disease with machine learning and 3D brain MRI
Nikita Goel, nikitago@usc.edu , Abstract NANO03.07
Biologically constrained deep neural networks to parse visual computations being performed in the primary visual cortex
Dan Butts, dab@umd.edu , Abstract PSTR149.16
Identifying the effective targets for deep brain stimulation: system identification using a simple recurrent neural network
Maral Kasiri, kasirim@uci.edu , Abstract PSTR154.16
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The Society for Neuroscience (SfN) is an organization of nearly 35,000 basic scientists and clinicians who study the brain and the nervous system.