Dynamical modeling, decoding, and control of multiscale brain network activity
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Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
