About this Research Topic
In recent years, machine learning and artificial intelligence algorithms have been utilized in solving many fascinating problems in different fields of science, including neuroscience. In this Research Topic, we are seeking to bring together researchers from machine learning and computational neuroscience and to stimulate collaboration between researchers in these fields. More specifically, this collection of articles is intended to cover recent directions and activities in the field of machine learning, especially the recent paradigm of deep learning, in neuroscience dedicated to analysis, diagnosis, and modeling of the neural mechanisms of brain functions.
We welcome submissions of original research papers from systems/cognitive and computational neuroscience, to neuroimaging and neural signal processing. Original research and reviews, as well as theoretical work, methods, and modeling articles are welcomed. The research work includes experimental studies using state-of-the-art in electrophysiology and neuroimaging as well as experimentally-based computational or theoretical work and biologically inspired neural networks.
We welcome submissions of original research papers from systems/cognitive and computational neuroscience, to neuroimaging and neural signal processing. Original research and reviews, as well as theoretical work, methods, and modeling articles are welcomed. The research work includes experimental studies using state-of-the-art in electrophysiology and neuroimaging as well as experimentally-based computational or theoretical work and biologically inspired neural networks.
Keywords: Machine Learning, Deep Learning, Image and Signal Processing, Brain Functional Modeling, Computational Neuroscience, Artificial Intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
