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This tutorial covers LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder and is a part of the Advanced Energy-Based Models module of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models IIEnergy-Based Models III, Energy-Based Models IV, and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:00:34
Speaker: : Alfredo Canziani

This tutorial covers the concepts of autoencoders, denoising encoders, and variational autoencoders (VAE) with PyTorch, as well as generative adversarial networks and code. It is a part of the Advanced energy based models modules of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models IEnergy-Based Models IIEnergy-Based Models IIIEnergy-Based Models IV, Energy-Based Models V, and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:07:50
Speaker: : Alfredo Canziani

This tutorial covers advanced concept of energy-based models. The lecture is a part of the Associative Memories module of the the Deep Learning Course at NYU's Center for Data Science. 

Difficulty level: Advanced
Duration: 1:12:00
Speaker: : Alfredo Canziani

This tutuorial covers the concept of graph convolutional networks and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 57:33
Speaker: : Alfredo Canziani

This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:10:22
Speaker: : Alfredo Canziani

This lecture covers the concepts of emulation of kinematics from observations and training a policy. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:01:21
Speaker: : Alfredo Canziani

This lecture covers the concept of predictive policy learning under uncertainty and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:14:44
Speaker: : Alfredo Canziani

This lecture continues on the topic of descent from the previous lesson, Optimization I. This lesson is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-7 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Difficulty level: Advanced
Duration: 1:51:32
Speaker: : Alfredo Canziani

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate
Duration: 5:17
Speaker: : Mike X. Cohen

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate
Duration: 11:37
Speaker: : Mike X. Cohen

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram. 

Difficulty level: Intermediate
Duration: 5:31
Speaker: : Mike X. Cohen

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons. 

Difficulty level: Intermediate
Duration: 13:48
Speaker: : Mike X. Cohen

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

Difficulty level: Intermediate
Duration: 12:16
Speaker: : Mike X. Cohen

In this lesson, users are shown how to create a spatial map of neuronal orientation tuning. 

Difficulty level: Intermediate
Duration: 13:11
Speaker: : Mike X. Cohen

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. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke

Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation. 

Difficulty level: Intermediate
Duration: 6:58
Speaker: : Marcus Ghosh

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

Difficulty level: Intermediate
Duration: 3:51
Speaker: : Dan Goodman

This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

Difficulty level: Intermediate
Duration: 10:01

The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).

Difficulty level: Intermediate
Duration: 7:43

This tutorial covers the fundamentals of collaborating with Git and GitHub.

Difficulty level: Intermediate
Duration: 2:15:50
Speaker: : Elizabeth DuPre