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Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

1:49:36
 
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Manage episode 360306039 series 2803422
内容由Machine Learning Street Talk (MLST)提供。所有播客内容(包括剧集、图形和播客描述)均由 Machine Learning Street Talk (MLST) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/ESrGqhf5CB

Twitter: https://twitter.com/MLStreetTalk

Chris Eliasmith is a renowned interdisciplinary researcher, author, and professor at the University of Waterloo, where he holds the prestigious Canada Research Chair in Theoretical Neuroscience. As the Founding Director of the Centre for Theoretical Neuroscience, Eliasmith leads the Computational Neuroscience Research Group in exploring the mysteries of the brain and its complex functions. His groundbreaking work, including the Neural Engineering Framework, Neural Engineering Objects software environment, and the Semantic Pointer Architecture, has led to the development of Spaun, the most advanced functional brain simulation to date. Among his numerous achievements, Eliasmith has received the 2015 NSERC "Polany-ee" Award and authored two influential books, "How to Build a Brain" and "Neural Engineering."

Chris' homepage:

http://arts.uwaterloo.ca/~celiasmi/

Interviewers: Dr. Tim Scarfe and Dr. Keith Duggar

TOC:

Intro to Chris [00:00:00]

Continuous Representation in Biologically Plausible Neural Networks [00:06:49]

Legendre Memory Unit and Spatial Semantic Pointer [00:14:36]

Large Contexts and Data in Language Models [00:20:30]

Spatial Semantic Pointers and Continuous Representations [00:24:38]

Auto Convolution [00:30:12]

Abstractions and the Continuity [00:36:33]

Compression, Sparsity, and Brain Representations [00:42:52]

Continual Learning and Real-World Interactions [00:48:05]

Robust Generalization in LLMs and Priors [00:56:11]

Chip design [01:00:41]

Chomsky + Computational Power of NNs and Recursion [01:04:02]

Spiking Neural Networks and Applications [01:13:07]

Limits of Empirical Learning [01:22:43]

Philosophy of Mind, Consciousness etc [01:25:35]

Future of human machine interaction [01:41:28]

Future research and advice to young researchers [01:45:06]

Refs:

http://compneuro.uwaterloo.ca/publications/dumont2023.html

http://compneuro.uwaterloo.ca/publications/voelker2019lmu.html

http://compneuro.uwaterloo.ca/publications/voelker2018.html

http://compneuro.uwaterloo.ca/publications/lu2019.html

https://www.youtube.com/watch?v=I5h-xjddzlY

  continue reading

151集单集

Artwork
icon分享
 
Manage episode 360306039 series 2803422
内容由Machine Learning Street Talk (MLST)提供。所有播客内容(包括剧集、图形和播客描述)均由 Machine Learning Street Talk (MLST) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/ESrGqhf5CB

Twitter: https://twitter.com/MLStreetTalk

Chris Eliasmith is a renowned interdisciplinary researcher, author, and professor at the University of Waterloo, where he holds the prestigious Canada Research Chair in Theoretical Neuroscience. As the Founding Director of the Centre for Theoretical Neuroscience, Eliasmith leads the Computational Neuroscience Research Group in exploring the mysteries of the brain and its complex functions. His groundbreaking work, including the Neural Engineering Framework, Neural Engineering Objects software environment, and the Semantic Pointer Architecture, has led to the development of Spaun, the most advanced functional brain simulation to date. Among his numerous achievements, Eliasmith has received the 2015 NSERC "Polany-ee" Award and authored two influential books, "How to Build a Brain" and "Neural Engineering."

Chris' homepage:

http://arts.uwaterloo.ca/~celiasmi/

Interviewers: Dr. Tim Scarfe and Dr. Keith Duggar

TOC:

Intro to Chris [00:00:00]

Continuous Representation in Biologically Plausible Neural Networks [00:06:49]

Legendre Memory Unit and Spatial Semantic Pointer [00:14:36]

Large Contexts and Data in Language Models [00:20:30]

Spatial Semantic Pointers and Continuous Representations [00:24:38]

Auto Convolution [00:30:12]

Abstractions and the Continuity [00:36:33]

Compression, Sparsity, and Brain Representations [00:42:52]

Continual Learning and Real-World Interactions [00:48:05]

Robust Generalization in LLMs and Priors [00:56:11]

Chip design [01:00:41]

Chomsky + Computational Power of NNs and Recursion [01:04:02]

Spiking Neural Networks and Applications [01:13:07]

Limits of Empirical Learning [01:22:43]

Philosophy of Mind, Consciousness etc [01:25:35]

Future of human machine interaction [01:41:28]

Future research and advice to young researchers [01:45:06]

Refs:

http://compneuro.uwaterloo.ca/publications/dumont2023.html

http://compneuro.uwaterloo.ca/publications/voelker2019lmu.html

http://compneuro.uwaterloo.ca/publications/voelker2018.html

http://compneuro.uwaterloo.ca/publications/lu2019.html

https://www.youtube.com/watch?v=I5h-xjddzlY

  continue reading

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