Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Explores the use of FNES in understanding deafness and brain plasticity in cochlear implant patients, highlighting the correlation between FNES data and speech understanding.
