By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
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.
Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.
Delves into neuron types, classification, challenges in reconstruction, staining techniques, and artifact correction, highlighting the importance of understanding brain complexity.
Explores neuron classification in in silico neuroscience, emphasizing challenges in reconstructing neuronal morphologies and the importance of accurate classifications.
