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Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory

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Manage episode 352767493 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu 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://podcastplayer.com/legal.

Sugandha Sharma is a Ph.D. candidate at MIT advised by Prof. Ila Fiete and Prof. Josh Tenenbaum. She explores the computational and theoretical principles underlying higher cognition in the brain by constructing neuro-inspired models and mathematical tools to discover how the brain navigates the world, or how to construct memory mechanisms that don’t exhibit catastrophic forgetting. In this episode, we chat about biologically inspired neural architectures, how memory could be implemented, why control theory is underrated and much more.

  continue reading

37 episodes

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iconShare
 
Manage episode 352767493 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu 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://podcastplayer.com/legal.

Sugandha Sharma is a Ph.D. candidate at MIT advised by Prof. Ila Fiete and Prof. Josh Tenenbaum. She explores the computational and theoretical principles underlying higher cognition in the brain by constructing neuro-inspired models and mathematical tools to discover how the brain navigates the world, or how to construct memory mechanisms that don’t exhibit catastrophic forgetting. In this episode, we chat about biologically inspired neural architectures, how memory could be implemented, why control theory is underrated and much more.

  continue reading

37 episodes

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