In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

This lecture covers concepts associated with neural nets, including rotation and squashing, and is a part of the Deep Learning Course at New York University's Center for Data Science (CDS).

This lecture covers the concept of neural nets training (tools, classification with neural nets, and PyTorch implementation) and is a part of the Deep Learning Course at NYU's Center for Data Science.

This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.

This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.

This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science. 

This tutorial covers the concept of training latent variable energy based models (LV-EBMs) and is is a part of the Deep Learning Course at NYU's Center for Data Science.

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.