Focuses on the development of 'eSee-Shells', chronic multimodal neural interface devices using transparent, inkjet-printed electrocorticography (ECoG) arrays.
Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.
