In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Dan Goodman

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data.

Difficulty level: Intermediate

Duration: 9:24

Speaker: : Marcus Ghosh

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs.

Difficulty level: Intermediate

Duration: 8:42

Speaker: : Marcus Ghosh

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

Difficulty level: Intermediate

Duration: 6:01

Speaker: : Dan Goodman

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

Difficulty level: Intermediate

Duration: 6:54

Speaker: : Marcus Ghosh

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?

Difficulty level: Intermediate

Duration: 9:34

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

This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.

Difficulty level: Intermediate

Duration: 56:49

Speaker: : Chris Gorgolewski

This is the Introductory Module to the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Difficulty level: Intermediate

Duration: 50:17

Speaker: : Yann LeCun and Alfredo Canziani

This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:51:03

Speaker: : Yann LeCun

This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:59:47

Speaker: : Yann LeCun and Alfredo Canziani

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

Difficulty level: Intermediate

Duration: 51:40

Speaker: : Yann LeCun

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.

Difficulty level: Intermediate

Duration: 1:09:12

Speaker: : Alfredo Canziani

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.

Difficulty level: Intermediate

Duration: 1:05:36

Speaker: : Alfredo Canziani

This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy-based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:51:30

Speaker: : Yann LeCun

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.

Difficulty level: Intermediate

Duration: 1:01:04

Speaker: : Alfredo Canziani

This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.

Difficulty level: Intermediate

Duration: 1:48:53

Speaker: : Yann LeCun

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.

Difficulty level: Intermediate

Duration: 1:04:48

Speaker: : Alfredo Canziani

- Bayesian networks (2)
- Clinical neuroinformatics (2)
- (-) Standards and Best Practices (1)
- Neuroimaging (19)
- Machine learning (9)
- Neuromorphic engineering (3)
- Tools (1)
- Animal models (1)
- Brain-hardware interfaces (1)
- Clinical neuroscience (1)
- (-) General neuroscience (15)
- (-) Computational neuroscience (12)
- Statistics (5)
- Computer Science (2)
- Genomics (8)
- Data science (2)
- Open science (4)