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  • Perspective
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Biological underpinnings for lifelong learning machines

Nature Machine Intelligence volume 4, pages 196–210 (2022)Cite this article

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Abstract

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 large extent unsolved. In this Perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence.

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Fig. 1: Key features required to achieve lifelong learning.
Fig. 2: Biological mechanisms that support lifelong learning.
Fig. 3: Neurogenesis.
Fig. 4: Episodic replay in the hippocampus of a rodent.
Fig. 5: Neuromodulatory systems in the brain.
Fig. 6: Biological systems use multiple levels of dynamical interactions.
Fig. 7: BEN: a non-neural bioelectric network (a mechanism used for control of growth and form during regeneration and repair) that can learn.
Fig. 8: Biomolecular perceptron circuit.
Fig. 9: Lifelong learning in reconfigurable organisms.
Fig. 10: Application of biologically inspired models for L2.

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Acknowledgements

This work was partly supported by the DARPA Lifelong Learning Machines programme. We wish to express our thanks to the technical leadership team of DARPA L2M, specifically R. McFarland, B. Epstein, R. McFarland and T. Senator. R. McFarland and B. Epstein offered several insights on organization of the paper, contributed in brainstorming sessions, and provided graphics suggestions. T. Senator seeded the idea to develop a review article. R. McFarland and other members of the L2M team spurred insightful discussions and provided feedback on the Perspective. We thank G. Vallabha, E. Johnson, M. Peot, F. Sha for reviewing the manuscript.

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Authors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, USA

    Dhireesha Kudithipudi, Anurag Daram, Peter Helfer, Tej Pandit & Nicholas Soures

  2. Intelligent Systems Laboratory, Teledyne Scientific, RTP, NC, USA

    Mario Aguilar-Simon & Andrew P. Brna

  3. Massachusetts Institute of Technology, Boston, MA, USA

    Jonathan Babb & Ron Weiss

  4. University of California at San Diego, La Jolla, CA, USA

    Maxim Bazhenov & Terrence J. Sejnowski

  5. Allen Discovery Center, Tufts University, Medford, MA, USA

    Douglas Blackiston, Sam Kriegman, Michael Levin & Santosh Manicka

  6. Wyss Institute, Harvard University, Cambridge, MA, USA

    Douglas Blackiston, Sam Kriegman & Michael Levin

  7. University of Vermont, Burlington, VT, USA

    Josh Bongard & Nick Cheney

  8. University of Southern California, Los Angeles, CA, USA

    Suraj Chakravarthi Raja, Ali Marjaninejad, Alice Parker, Darío Urbina-Meléndez & Francisco J. Valero-Cuevas

  9. University of British Columbia, Vancouver, BC, Canada

    Jeff Clune

  10. Columbia University, New York, NY, USA

    Stefano Fusi

  11. University of Chicago, Chicago, IL, USA

    Leslie Kay

  12. HRL Laboratories, Malibu, CA, USA

    Nicholas Ketz & Praveen K. Pilly

  13. Georgia Institute of Technology, Atlanta, GA, USA

    Zsolt Kira

  14. Vanderbilt University, Nashville, TN, USA

    Soheil Kolouri

  15. University of California, Irvine, CA, USA

    Jeffrey L. Krichmar, Bruce McNaughton, Zaneta Navratilova & Xinyun Zou

  16. Argonne National Laboratory, Lemont, IL, USA

    Sandeep Madireddy & Angel Yanguas-Gil

  17. The University of Texas at Austin, Austin, TX, USA

    Risto Miikkulainen

  18. IT University of Copenhagen, Copenhagen, Denmark

    Sebastian Risi

  19. Salk Institute for Biological Studies, La Jolla, CA, USA

    Terrence J. Sejnowski

  20. Loughborough University, Loughborough, UK

    Andrea Soltoggio

  21. Rochester Institute of Technology, Rochester, NY, USA

    Nicholas Soures

  22. Baylor College of Medicine, Houston, TX, USA

    Andreas S. Tolias & Gido M. van de Ven

  23. Johns Hopkins University, Baltimore, MA, USA

    Joshua T. Vogelstein

  24. Sandia National Laboratories, Albuquerque, NM, USA

    Felix Wang

  25. University of Massachusetts, Amherst, MA, USA

    Hava Siegelmann

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Correspondence to Dhireesha Kudithipudi.

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Nature Machine Intelligence thanks Nabil Imam and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

A brief explanation of the biologically inspired models mentioned in the article; and metrics that have been used to assess specific aspects of L2 performance.

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Kudithipudi, D., Aguilar-Simon, M., Babb, J. et al. Biological underpinnings for lifelong learning machines. Nat Mach Intell 4, 196–210 (2022). https://doi.org/10.1038/s42256-022-00452-0

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