By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores the development of a mathematical model of the brain, focusing on brain organization and dynamics, including neuronal activity patterns and emergent phenomena.
Explores vision research, optic nerve regeneration, and bio-realistic models in the mouse visual cortex, focusing on optic nerve stimulation and visual learning.
Explores brain circuits for sensory perception and external representation, covering thalamus communication, energy-saving mechanisms, inhibitory control, and time perception.
Explores data from the mouse primary visual cortex, focusing on visual information encoding, transformation, diversity of responses, and locomotion modulation.
Explores how slow cortical oscillations in the frontal cortex coordinate brain networks and impact memory processes, with a focus on cognitive control and healthy aging.
Explores neuroimaging basics, brain network scales, connectivity, history, and physics, emphasizing the importance of understanding data at different scales.
Explores Graph Signal Processing applied to brain networks, emphasizing the relationship between brain function and structure using methods like Graph Fourier Transform and Structural-Decoupling Index.
