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#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: Patrick's Blog: Twitter: Neuromatch Academy: Links: TabNine Code Completion (Referral): YouTube: Twitter: Discord: BitChute: LinkedIn: BiliBili: If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: Patreon: Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n
