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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.
Explores neural signal processing, MRI techniques, and imaging validation, emphasizing structural and functional connectivity and clinical applications.
Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
