3rd Workshop on Machine Learning in Neuroscience (MALENE) Francisco J. Martinez-Murcia, Universidad de Granada (Spain).
Juan M. Górriz, Universidad de Granada (Spain).
Javier RamÃrez, Universidad de Granada (Spain).
Andrés Ortiz, Universidad de Málaga (Spain).
In contemporary neuroscience research, machine learning has emerged as a potent tool to gain deeper insights into the complexities of the human brain. This union of machine learning and statistics holds great promise for the study of psychological, psychiatric and neurological conditions, cognitive and brain aging, brain structure, connectivity, and behavior. Machine learning and deep learning algorithms offer the means to uncover hidden patterns of abnormal brain activity, identify neuroimaging biomarkers, and elucidate the physiological mechanisms underpinning brain and behavior. A critical aspect of this work lies in the rigorous validation of machine learning models within the field of neuroimaging. Robust methodologies for assessing the significance of machine learning results ensure that findings are not spurious but firmly grounded in replication, validation, and hypothesis-driven confirmation.
At the intersection of these disciplines, there exists an opportunity to bridge two distinct scientific communities: the machine learning community, which encompasses experts in deep learning, pattern recognition, and artificial intelligence, and the neuroscience community. Our workshop provides a platform for researchers to showcase their recent innovations, from predictive modeling in neurodegenerative diseases to the application of interpretable machine learning (XAI) in healthcare. Researchers are invited to present their latest advances and preliminary work, offering fresh perspectives on the computational principles of intelligence and data science as applied to neuroscience.
Our collective endeavor seeks to explore the depths of the neural system, pushing the boundaries of knowledge and redefining the frontiers of neuroscience. The workshop’s thematic focus spans a range of essential topics, related, but not limited, to the following list of topics of interest: