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Neural constraints on learning

Author

Listed:
  • Patrick T. Sadtler

    (University of Pittsburgh
    Center for the Neural Basis of Cognition
    Systems Neuroscience Institute, University of Pittsburgh)

  • Kristin M. Quick

    (University of Pittsburgh
    Center for the Neural Basis of Cognition
    Systems Neuroscience Institute, University of Pittsburgh)

  • Matthew D. Golub

    (Center for the Neural Basis of Cognition
    Carnegie Mellon University)

  • Steven M. Chase

    (Center for the Neural Basis of Cognition
    Carnegie Mellon University)

  • Stephen I. Ryu

    (Stanford University
    Palo Alto Medical Foundation)

  • Elizabeth C. Tyler-Kabara

    (University of Pittsburgh
    University of Pittsburgh
    University of Pittsburgh)

  • Byron M. Yu

    (Center for the Neural Basis of Cognition
    Carnegie Mellon University
    Carnegie Mellon University)

  • Aaron P. Batista

    (University of Pittsburgh
    Center for the Neural Basis of Cognition
    Systems Neuroscience Institute, University of Pittsburgh)

Abstract

During learning, the new patterns of neural population activity that develop are constrained by the existing network structure so that certain patterns can be generated more readily than others.

Suggested Citation

  • Patrick T. Sadtler & Kristin M. Quick & Matthew D. Golub & Steven M. Chase & Stephen I. Ryu & Elizabeth C. Tyler-Kabara & Byron M. Yu & Aaron P. Batista, 2014. "Neural constraints on learning," Nature, Nature, vol. 512(7515), pages 423-426, August.
    • Handle: RePEc:nat:nature:v:512:y:2014:i:7515:d:10.1038_nature13665
    DOI: 10.1038/nature13665
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    Citations

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    Cited by:

    1. Svenja Melbaum & Eleonora Russo & David Eriksson & Artur Schneider & Daniel Durstewitz & Thomas Brox & Ilka Diester, 2022. "Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Ege Altan & Sara A Solla & Lee E Miller & Eric J Perreault, 2021. "Estimating the dimensionality of the manifold underlying multi-electrode neural recordings," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-23, November.
    3. Rishabh Chandak & Baranidharan Raman, 2023. "Neural manifolds for odor-driven innate and acquired appetitive preferences," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    4. Josh Merel & Donald M Pianto & John P Cunningham & Liam Paninski, 2015. "Encoder-Decoder Optimization for Brain-Computer Interfaces," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    5. Benjamin R Cowley & Matthew A Smith & Adam Kohn & Byron M Yu, 2016. "Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-31, December.
    6. Ryan C Williamson & Benjamin R Cowley & Ashok Litwin-Kumar & Brent Doiron & Adam Kohn & Matthew A Smith & Byron M Yu, 2016. "Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-27, December.
    7. Tanner C Dixon & Christina M Merrick & Joni D Wallis & Richard B Ivry & Jose M Carmena, 2021. "Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-35, November.
    8. Han-Lin Hsieh & Maryam M Shanechi, 2018. "Optimizing the learning rate for adaptive estimation of neural encoding models," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-34, May.
    9. Sravani Kondapavulur & Stefan M. Lemke & David Darevsky & Ling Guo & Preeya Khanna & Karunesh Ganguly, 2022. "Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    10. Jonathan Eden & Mario Bräcklein & Jaime Ibáñez & Deren Yusuf Barsakcioglu & Giovanni Di Pino & Dario Farina & Etienne Burdet & Carsten Mehring, 2022. "Principles of human movement augmentation and the challenges in making it a reality," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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