Tag: ECE

Artificial neural networks meet biological neural networks: designing personalized stimulation for the data-driven control of neural dynamics

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.

Improving Reinforcement Learning Efficacy with Causality, Trajectories, and the Value of Information

Reinforcement learning (RL) is very slow and does not scale up well to high dimensional spaces. This proposal employs causality to go beyond correlation learning to reduce the amount of data necessary to train RL. Pointwise interactions with the environment will be substituted by trajectories to speed up training, while the value of information optimizes the exploration exploitation dilemma.