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On synaptic learning rules for spiking neurons - with Friedemann Zenke - #11
Manage episode 414872240 series 3562348
Content provided by Gaute Einevoll. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Gaute Einevoll or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Today’s AI is largely based on supervised learning of neural networks using the backpropagation-of-error synaptic learning rule. This learning rule relies on differentiation of continuous activation functions and is thus not directly applicable to spiking neurons.
Today’s guest has developed the algorithm SuperSpike to address the problem. He has also recently developed a biologically more plausible learning rule based on self-supervised learning. We talk about both.
15 episodes
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Manage episode 414872240 series 3562348
Content provided by Gaute Einevoll. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Gaute Einevoll or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Today’s AI is largely based on supervised learning of neural networks using the backpropagation-of-error synaptic learning rule. This learning rule relies on differentiation of continuous activation functions and is thus not directly applicable to spiking neurons.
Today’s guest has developed the algorithm SuperSpike to address the problem. He has also recently developed a biologically more plausible learning rule based on self-supervised learning. We talk about both.
15 episodes
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Similar to Theoretical Neuroscience Podcast
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