Neuroscience is a multidisciplinary science concerned with the study of the structure and function of the nervous system. It encompasses evolution, development, cellular and molecular biology, neural circuits, cognition, human behavior, neuron physiology, anatomy, and pharmacology of the nervous system.

In recent years, researchers and scientists have been empowered by deep/machine learning algorithms and approaches as a branch of theoretical computer science for discovering the statistical patterns in large datasets for a wide variety of tasks and applications such as medicine, neuroscience, disease diagnosis, and computer vision.

Recent progress in deep/machine learning has developed the ability to solve complex problems with the highest accuracy and efficiency in various research areas.

In this Special Issue, we seek to bring together researchers from deep/machine learning and computational neuroscience and stimulate collaboration between researchers in these fields. In particular, this Special Issue intends to cover the most recent and state-of-the-art activities in the area of machine learning and deep learning in neuroscience dedicated to analysis, disease diagnosis, and modeling of the neural mechanisms of brain functions. We welcome submission of innovative and novel research papers from neuroimaging, blood-oxygen level dependent fMRI signal analysis, removing physiological noise, new hemodynamic response functions, parcellation, brain functional networks analysis, brain disease diagnosis, and new DL and ML algorithms inspired by brain learning mechanisms.

Potential topics include but are not limited to the following:

• Using deep/machine learning (DL/ML) to gain insight into how the brain learns
• DL/ML techniques and approaches for automating the analysis of large neuroscience datasets
• DL/ML concepts combined with neuroscience theories as a prediction tool for nervous system function and uncovering general principles
• Brain tumor detection and classification using DL/ML algorithms
• Prediction of neurodegenerative diseases based on brain functional connectivity and ML
• Image translation
• Application of DL/ML algorithms for MRI contrast improvement
• New indices investigation for brain diseases such as Alzheimer’s and Parkinson’s diseases
• Brain image de-noising using DL/ML algorithms
• Optimized ML-based disease diagnosis