Brain-inspired Machine Learning and Computation for Brain-Behavior Analysis

242.2K

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122

authors

22

articles

Brain-inspired Machine Learning and Computation for Brain-Behavior Analysis

242.2K

views

122

authors

22

articles

Editors

3

Rong Chen

University of Maryland

Tianyi Yan

Beijing Institute of Technology

Yiannis Ventikos

University College London

Impact

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About

Machine learning, artificial intelligence, and deep learning are revolutionizing the field of neuroscience by enabling machines to detect complex patterns, automatically generate hypotheses, and create interpretable models revealing biological mechanisms. Using machine learning, we can analyze neuroscientific data to provide a better understanding of both the normal cognitive and pathological processes in brain disorders, leading to new treatment plans for them. This knowledge also will help us design next-generation brain-inspired artificial intelligence, potentially leading to a harmonized society of humans and robots.

The main focus of this Research Topic is brain-inspired machine learning algorithms and computational models which links brain and behavior, including machine learning algorithms, mathematical models, signal and image processing, computer vision, big data analytics, and statistical analyses. These algorithms or models can reveal the mechanism underlying normal or diseased brain processes, or mimic some aspect of brain computing (simulation). This Research Topic will seek contributions that focus on the following topics: (1) decoding neural activities using advanced interpretable machine learning methods, (2) network analysis methods to model interactions among different components, including brain structural and functional network analysis, (3) mathematical/computational models explaining a cognitive process such as rewarding.

Potential topics include, but are not limited to:
• Neural decoding
• Models for cognitive or emotional processes
• Machine learning methods to analyze neural activity data such as calcium imaging, EEG, MR
• Brain network analysis
• Temporal modeling of neural activity data
• Multiscale analysis
• Integrated system of neural decoding and neuromodulation
• Brain-inspired computing and devices
• Multimodal neuroimaging and data fusion
• Scalable, high-performance, or real-time algorithms to process massive brain datasets

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Editors

Rong Chen

University of Maryland

Tianyi Yan

Beijing Institute of Technology

Yiannis Ventikos

University College London

Impact

242,155 Total views

195,844 Article views

37,686 Article downloads

8,625 Topic views

Published In

Frontiers in Computational Neuroscience

Frontiers in Neuroinformatics

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