The project aims to apply modern time series forecasting techniques, specifically transformers and mixture of linear systems, to predict neural activity. The goal is to create predictive embeddings of neural activity, providing a compact, yet rich, representation of the brain’s dynamics. By integrating both transformer-based and linear system models, we aim to capture the complex temporal dependencies in neural data and generate accurate forecasts. This project offers a focused exploration of the intersection between machine learning and neuroscience, with an emphasis on time series forecasting. Please, have a look here for more context about potential research projects.
