Biological underpinnings for lifelong learning machines

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a la...

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Bibliographic Details
Main Authors: Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K Pilly, Sebastian Risi, Terrence J Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S Tolias, Darío Urbina-Meléndez, Francisco J Valero-Cuevas, Gido M van de Ven, Joshua T Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava Siegelmann
Format: Default Article
Published: 2022
Subjects:
Computer science
Intelligence
Learning algorithms
Online Access:https://hdl.handle.net/2134/19453778.v1
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https://hdl.handle.net/2134/19453778.v1

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