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Computational principles of memory

Nature Neuroscience volume 19, pages 394–403 (2016)Cite this article

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Abstract

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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Figure 1: Stable states from positive feedback.
Figure 2: Circuit mechanisms for persistent states.
Figure 3: Long-term maintenance of synapse size.

Katie Vicari/Nature Publishing Group

Figure 4: Robustness of persistent activity architectures.
Figure 5: The tradeoff between capacity and robustness.
Figure 6: Complexity cost of storing a continuous variable in a set of well-separated discrete attractors.

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Acknowledgements

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.

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  1. Center for Learning and Memory and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA

    Rishidev Chaudhuri & Ila Fiete

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  1. Rishidev Chaudhuri
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Correspondence to Ila Fiete.

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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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