Principal Investigator: Shreya Saxena
Co-PI: YK Yoon
Sponsor: UF AI Research Catalyst Fund
Start Date: January 1, 2021
End Date: December 31, 2021
Personalized neurostimulation using data-driven models has enormous potential to restore neural activity towards health. However, the inference of individualized high- dimensional dynamical models from data remains challenging due to their under-constrained nature. Moreover, the design of stimulation strategies requires exploration of extremely large parameter spaces. To address these issues, we will leverage the wealth of data that multi-subject experiments provide, as well as the computational resources newly available at UF.
We will develop new AI methods that utilize in-vivo neural responses to design and implement personalized stimulation in real-time. These will be developed using functional Magnetic Resonance Imaging (fMRI) datasets collected in-house to examine neural activity related to memory / cognition. We will (Aim 1) build recurrent neural networks of memory-related neural activity, and (Aim 2) design personalized brain stimulation to achieve a memory-enhanced neural response. Promising stimulation strategies will be validated in- silico on multiple datasets and finally in-vivo using an fMRI-compatible neural probe.More Information: https://news.ufl.edu/2020/12/artificial-intelligence-research-catalyst-fund-/