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Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

Author

Listed:
  • Barbara Feulner

    (Imperial College London)

  • Matthew G. Perich

    (Université de Montréal)

  • Raeed H. Chowdhury

    (University of Pittsburgh)

  • Lee E. Miller

    (Northwestern University
    Northwestern University
    Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab)

  • Juan A. Gallego

    (Imperial College London)

  • Claudia Clopath

    (Imperial College London)

Abstract

Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (Hinput) rather than from changes in local connectivity (Hlocal), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation. As expected, Hinput resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to Hlocal only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

Suggested Citation

  • Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32646-w
    DOI: 10.1038/s41467-022-32646-w
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    References listed on IDEAS

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    1. Valerio Mante & David Sussillo & Krishna V. Shenoy & William T. Newsome, 2013. "Context-dependent computation by recurrent dynamics in prefrontal cortex," Nature, Nature, vol. 503(7474), pages 78-84, November.
    2. Javier F. Medina & William L. Nores & Michael D. Mauk, 2002. "Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses," Nature, Nature, vol. 416(6878), pages 330-333, March.
    3. Anthony M. Zador, 2019. "A critique of pure learning and what artificial neural networks can learn from animal brains," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    4. Brian DePasquale & Christopher J Cueva & Kanaka Rajan & G Sean Escola & L F Abbott, 2018. "full-FORCE: A target-based method for training recurrent networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    5. Tonghui Xu & Xinzhu Yu & Andrew J. Perlik & Willie F. Tobin & Jonathan A. Zweig & Kelly Tennant & Theresa Jones & Yi Zuo, 2009. "Rapid formation and selective stabilization of synapses for enduring motor memories," Nature, Nature, vol. 462(7275), pages 915-919, December.
    6. Guillaume Bellec & Franz Scherr & Anand Subramoney & Elias Hajek & Darjan Salaj & Robert Legenstein & Wolfgang Maass, 2020. "A solution to the learning dilemma for recurrent networks of spiking neurons," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    7. Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
    8. Robert C. Froemke & Yang Dan, 2002. "Spike-timing-dependent synaptic modification induced by natural spike trains," Nature, Nature, vol. 416(6879), pages 433-438, March.
    9. Lee Susman & Naama Brenner & Omri Barak, 2019. "Stable memory with unstable synapses," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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    Cited by:

    1. Joanna C. Chang & Matthew G. Perich & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2024. "De novo motor learning creates structure in neural activity that shapes adaptation," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Hao Guo & Shenbing Kuang & Alexander Gail, 2025. "Sensorimotor environment but not task rule reconfigures population dynamics in rhesus monkey posterior parietal cortex," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    3. Joseph Pemberton & Paul Chadderton & Rui Ponte Costa, 2024. "Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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