Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Explores optogenetic tools for probing neuronal activity using light and calcium-gated tools, discussing their applications in neuroscience and beyond.
Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
Explores the water window in electrochemistry, charge injection capacity, neural stimulation safety, electrode materials, and optimal stimulation characteristics.