How Biological Synapses Self-Assemble Gradient Learning
Name
SAM2026_ver3.2t.pdf
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3.12 MB
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Checksum (MD5)
078350687a1cb95a7189eed1e37a83d3
Author(s)
Liao, Qianli
Ziyin, Liu
Gan, Yulu
Cheung, Brian
Harnett, Mark
Poggio, Tomaso
Date Issued
October 9, 2026
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Citation
Q. Liao,L. Ziyin,Y. Gan,B. Cheung,M.T. Harnett, & T. Poggio, How biological synapses self-assemble gradient learning, Proc. Natl. Acad. Sci. U.S.A. 123 (41) e2606245123, https://doi.org/10.1073/pnas.2606245123 (2026).
Version
Author's final manuscript
Abstract
Existing models of learning in the brain explain how given circuits learn, but not how biology could assemble those circuits in the first place. To address this gap, we formulate Self-Assembling Learning (SAL)---the study of how learning systems can emerge from lower-level interactions---and introduce one example mechanism, the Self-Assembling Motif (SAM). SAM is self-assembling at two scales: the motif emerges from initially random connectivity under heterosynaptic plasticity rules, and networks of SAMs, composed hierarchically, self-organize into dynamics that provably approximate a generalized form of stochastic gradient descent---matching backpropagation-level performance. This suggests that biological learning need not be prescribed but can emerge from local rules---and to a far greater extent than previously thought.
Subjects
Neural networks
Biological learning
Gradient descent
Synaptic plasticity
Self-assembly
MIT Department
McGovern Institute for Brain Research at MIT
Center for Brains, Minds, and Machines
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/pnas.2606245123
