CTN: Eva Dyer 

Title: Large-scale pretraining on neural data allows for transfer across individuals, tasks and species

Abstract: As neuroscience datasets grow in size and complexity, integrating diverse data sources to achieve a comprehensive understanding of brain function presents both an opportunity and a challenge. In this talk, I will introduce our approach to developing a multi-source foundation model for neuroscience, utilizing large-scale pretraining on neural data from various tasks, brain regions, and species. These models are designed to enable seamless transfer learning across individuals, tasks, and species, thereby enhancing data efficiency and advancing the capabilities of neural decoding technologies. By integrating diverse datasets, our aim is to uncover the common neural functions that underlie a wide range of tasks and brain regions, providing a deeper understanding of brain function and informing future brain-machine interface applications.

Zoom:
https://columbiauniversity.zoom.us/j/97505761667?pwd=KkvqBSag7VPFebf8eyqKpqvdVPbaHn.1
passcode: ctn