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full-FORCE: A target-based method for training recurrent networks

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  • Brian DePasquale
  • Christopher J Cueva
  • Kanaka Rajan
  • G Sean Escola
  • L F Abbott

Abstract

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable “target” dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0191527
    DOI: 10.1371/journal.pone.0191527
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    References listed on IDEAS

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    1. Ranulfo Romo & Carlos D. Brody & Adrián Hernández & Luis Lemus, 1999. "Neuronal correlates of parametric working memory in the prefrontal cortex," Nature, Nature, vol. 399(6735), pages 470-473, June.
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    Cited by:

    1. 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.

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