#deeplearning #brain #neuroscience

Originally, Deep Learning sprang into existence inspired by how the brain processes information, but the two fields have diverged ever since. However, given that deep models can solve many perception tasks with remarkable accuracy, is it possible that we might be able to learn something about how the brain works by inspecting our models? I speak to Patrick Mineault about his blog post "2021 in review: unsupervised brain models" and we explore why neuroscientists are taking interest in unsupervised and self-supervised deep neural networks in order to explain how the brain works. We discuss a series of influential papers that have appeared last year, and we go into the more general questions of connecting neuroscience and machine learning.

OUTLINE:

0:00 - Intro & Overview

6:35 - Start of Interview

10:30 - Visual processing in the brain

12:50 - How does deep learning inform neuroscience?

21:15 - Unsupervised training explains the ventral stream

30:50 - Predicting own motion parameters explains the dorsal stream

42:20 - Why are there two different visual streams?

49:45 - Concept cells and representation learning

56:20 - Challenging the manifold theory

1:08:30 - What are current questions in the field?

1:13:40 - Should the brain inform deep learning?

1:18:50 - Neuromatch Academy and other endeavours

Blog Post: https://xcorr.net/2021/12/31/2021-in-...

Patrick's Blog: https://xcorr.net/

Twitter: https://twitter.com/patrickmineault

Neuromatch Academy: https://academy.neuromatch.io/

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