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BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding

BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding

FromBrain Inspired


BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding

FromBrain Inspired

ratings:
Length:
89 minutes
Released:
Dec 11, 2023
Format:
Podcast episode

Description

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Welcome to another special panel discussion episode.



I was recently invited to moderate at discussion amongst 6 people at the annual Aspirational Neuroscience meetup. Aspirational Neuroscience is a nonprofit community run by Kenneth Hayworth. Ken has been on the podcast before on episode 103. Ken helps me introduce the meetup and panel discussion for a few minutes. The goal in general was to discuss how current and developing neuroscience technologies might be used to decode a nontrivial memory from a static connectome - what the obstacles are, how to surmount those obstacles, and so on.



There isn't video of the event, just audio, and because we were all sharing microphones and they were being passed around, you'll hear some microphone type noise along the way - but I did my best to optimize the audio quality, and it turned out mostly quite listenable I believe.




Aspirational Neuroscience



Panelists:

Anton Arkhipov, Allen Institute for Brain Science.

@AntonSArkhipov





Konrad Kording, University of Pennsylvania.

@KordingLab





Tomás Ryan, Trinity College Dublin.

@TJRyan_77





Srinivas Turaga, Janelia Research Campus.



Dong Song, University of Southern California.

@dongsong





Zhihao Zheng, Princeton University.

@zhihaozheng








0:00 - Intro
1:45 - Ken Hayworth
14:09 - Panel Discussion
Released:
Dec 11, 2023
Format:
Podcast episode

Titles in the series (99)

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.