By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
