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Cited by 236Domain Adaptation and Few-Shot LearningNeural Networks and ApplicationsMemory and Neural Mechanisms

Biological underpinnings for lifelong learning machines

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Kudithipudi, Dhireesha, Aguilar-Simon, Mario, Babb, Jonathan, Bazhenov, Maxim, Blackiston, Douglas, Bongard, Josh, Brna, Andrew P., Chakravarthi Raja, Suraj +35 more·2022·Nature Machine Intelligence
DOI 10.1038/s42256-022-00452-0
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  • Some incremental learning research, such as the method of Kudithipudi et al. that emulates the Drosophila mushroom body's mechanisms, attempts to enhance model memory capabilities through bio-inspired intelligence.

    “Currently, some IL research, such as the method proposed by ZKudithipudi et al. [115] that emulates the Drosophila's mushroom body's mechanisms, attempts to enhance model memory capabilities by bio-inspired intelligence.”

    From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [115]

    The model description under 5 Conclusions (page unavailable) describes ModNet as inspired by the insect mushroom body, supporting the bio-inspired learning aspect. But the paper reviews ModNet rather than proposing it as a new method by Kudithipudi et al.; it also says ModNet is prone to forgetting prior knowledge, so it does not establish improved memory retention. (LLM, conf=0.88)

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Authors
Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A. P., Chakravarthi Raja, S., Cheney, N., Clune, J., Daram, A., Fusi, S., Helfer, P., Kay, L., Ketz, N., Kira, Z., Kolouri, S., Krichmar, J. L., Kriegman, S., Levin, M., Madireddy, S., Manicka, S., Marjaninejad, A., McNaughton, B., Miikkulainen, R., Navratilova, Z., Pandit, T., Parker, A., Pilly, P. K., Risi, S., Sejnowski, T. J., Soltoggio, A., Soures, N., Tolias, A. S., Urbina-Meléndez, D., Valero-Cuevas, F. J., van de Ven, G. M., Vogelstein, J. T., Wang, F., Weiss, R., Yanguas-Gil, A., Zou, X., & Siegelmann, H.
Affiliations
  • The University of Texas at San Antonio (US)
  • Massachusetts Institute of Technology (US)
  • University of California San Diego (US)
  • Tufts University (US)
  • University of Vermont (US)
  • University of British Columbia (CA)
  • Columbia University (US)
  • University of Chicago (US)
  • HRL Laboratories (United States) (US)
  • Georgia Institute of Technology (US)
  • Vanderbilt University (US)
  • University of California, Irvine (US)
  • Argonne National Laboratory (US)
  • University of Southern California (US)
  • The University of Texas at Austin (US)
  • IT University of Copenhagen (DK)
  • Loughborough University (GB)
  • Rochester Institute of Technology (US)
  • Baylor College of Medicine (US)
  • Johns Hopkins University (US)
  • Sandia National Laboratories (US)
  • University of Massachusetts Amherst (US)
Corresponding author
Kudithipudi, Dhireesha
ORCID
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2022-03-23
Volume
4
Issue
3
Pages
196-210
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-022-00452-0
OpenAlex
W4220918847
Cited by
236
References
22
Citations by year
2026: 34 · 2025: 82 · 2024: 63 · 2023: 44 · 2022: 13
FWCI
16.64
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Machine Intelligence (publishedVersion)
  • Lirias (KU Leuven) (submittedVersion, other-oa)
  • Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
Defense Advanced Research Projects Agency
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords
lifelong learning, continual learning, biologically inspired learning, neuromodulation, neuroplasticity, artificial intelligence

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