Frontiers in Computational Neuroscience is a multidisciplinary journal that focuses on the theoretical modeling of brain function and encourages multidisciplinary interactions between theoretical and experimental neuroscience.

Led by Specialty Chief Editors Misha Tsodyks (Weizmann Institute of Science, Israel), and Si Wu (Peking University, China) Frontiers in Computational Neuroscience welcomes research contributions in various domains of computational neuroscience, which bridge the gap between theoretical models and experimental studies to validate them. Topics include, but are not limited to:

biophysically motivated realistic simulations of neurons and synapses
experimental studies that validate and test theoretical conclusions
high-level abstract models of inference and decision making
use of computational tools and AI for prediction and better detection of mental and neurological disorders.

The journal supports and advances the Sustainable Development Goal (SDG) 3: good health and well-being by promoting a deeper understanding of brain function and fostering research and collaboration in the field. This contributes to the development of new knowledge and technologies that can potentially improve mental health, neurological disorders, and overall well-being, aligning with the broader goal of ensuring healthy lives and promoting well-being for all at all ages.

Manuscripts relating to topics such as the use of artificial intelligence (including neuronal networks method) that does not have any relevant theoretical model implications or practical applications for the neuroscience field are not suitable for publication in this journal.

Frontiers in Computational Neuroscience is dedicated to advancing developments in the field of computational neuroscience by allowing unrestricted access to articles and communicating scientific knowledge to researchers and the public alike, enabling the scientific breakthroughs of the future.

Authors

Frontiers in Computational Neuroscience