Exploring and modeling human cognition through deep learning

Some of us recall our dreams very often, while other hardly ever remember them. The neuroscience of sleep and dream research is a thriving field with still many unanswered questions. Differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood. In this internship, we will address this question using state-of-the-art machine learning tools. In particular, we will frame it as a classification problem where we apply deep convolutional neural networks (CNN) to sleep EEG recordings in order to predict whether subjects belong to a high or low dream recall group (HDR and LDR resp.). In addition, to explore the neural properties that the AI approach used for successful discrimination, we will test several techniques for the visualization of the feature space learned by the network. This project lies at the intersection between AI and Neuroscience and carries the potential to advance our understanding of dream-related cognitive processes y combining EEG and data-driven machine learning tools.

Faculty Supervisor:

Karim Jerbi

Student:

Partner:

Birla Institute of Technology and Science, Pilani

Discipline:

Computer science

Sector:

Life Sciences (not health); Information and Communications Technology; Health and Related Sciences & Technology

University:

Université de Montréal

Program: