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.
Delves into neuron types, classification, challenges in reconstruction, staining techniques, and artifact correction, highlighting the importance of understanding brain complexity.
Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.
Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.