The ability to store and later use information is essential for a variety of adaptive behaviors, including integration, learning, generalization, prediction and inference. In this Review, we survey theoretical principles that can allow the brain to construct persistent states for memory. We identify requirements that a memory system must satisfy and analyze existing models and hypothesized biological substrates in light of these requirements. We also highlight open questions, theoretical puzzles and problems shared with computer science and information theory.
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Attractor dynamics with activity-dependent plasticity capture human working memory across time scales
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We thank K. Harris and K. Raab-Graham for discussions on subcellular substrates of memory. We are grateful to S. Fusi, J. Lisman, J. Murray, X.-J. Wang, and to A. Das, I. Kanitscheider, B. Kriener and J. Widloski of the Fiete group for comments on the manuscript. This work was funded in part by grants from the Simons Collaboration on the Global Brain, the ONR-Young Investigator Program (N000141310529), and the McKnight Foundation to I.F.
The authors declare no competing financial interests.
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Chaudhuri, R., Fiete, I. Computational principles of memory. Nat Neurosci 19, 394–403 (2016). https://doi.org/10.1038/nn.4237
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