Explores the application of computational neuroscience in neuroprosthetics, focusing on predicting intended arm movements based on spike times and the importance of systematic parameter optimization.
Explores miniaturized CMOS interfaces for neural recording and discusses spatial and temporal resolution, spike sorting, and wireless neural-interface-on-chip systems.
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.
