Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

14 papers of 11,817Sort Recent · Most cited
  1. 2025
    Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learningOscar C. González, Ryan Golden, Erik Delanois … Maxim BazhenovbioRxiv
  2. 2025
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  5. 2025
    Assembly-based computations through contextual dendritic gating of plasticitySebastian Onasch, Christoph Miehl, M. M. Miękus, Julijana GjorgjievabioRxiv
  6. 2025
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  8. 2025
    Neural signatures of motor memories emerge in neural network modelsJoanna C. Chang, C. Clopath, J. A. GallegobioRxiv
  9. 2025
    A canonical cortical electronic circuit for neuromorphic intelligenceMaryada, C. De Luca, Arianna Rubino … Giacomo IndiveribioRxiv
  10. 2025
    Recasting adaptation as strategy inferenceSami Beaumont, Mehdi Khamassi, Philippe DomenechbioRxiv
  11. 2025
  12. 2025
    An invariant schema emerges within a neural network during hierarchical learning of visual boundariesJames R. Elder, Jie Zheng, Lydia B. Shimelis … Mi-Lo M. LinbioRxiv
  13. 2025
    Mental Schema Reduces Cognitive Load and Facilitates Emergence of Novel Responses in Mice and Artificial Neural NetworksVikram Pal Singh, Shruti Shridhar, Shankanava Kundu … Balaji JayaprakashbioRxiv
  14. 2025
    Addressing the “open world”: detecting and segmenting pollen on palynological slides with deep learningJennifer T. Feng, Sandeep Puthanveetil Satheesan, Shuqing Kong … S. PunyasenabioRxiv
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.