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Recurrent networks learn to tell time

Nature Neuroscience volume 16, pages 772–774 (2013)Cite this article

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Chaotic networks produce rich temporal dynamics that could be useful for timing, but are extremely sensitive to perturbations. Work now shows that a learning rule for the weights of a chaotic recurrent network can stabilize time-varying activity patterns. This result can be used to train output units to produce generic timed responses.

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Figure 1: Effect of learning on the dynamics of the network.

References

  1. Gallistel, C.R. The Organization of Learning (Bradford Book, 1993).

    Google Scholar 

  2. Laje, R. and Buonomano, D.V. Nat. Neurosci. 16, 925–933 (2013).

    Article  CAS  Google Scholar 

  3. Durstewitz, D. J. Neurosci. 23, 5342–5353 (2003).

    Article  CAS  Google Scholar 

  4. Komura, Y. et al. Nature 412, 546–549 (2001).

    Article  CAS  Google Scholar 

  5. Matell, M.S. & Meck, W. Brain Res. Cogn. Brain Res. 21, 139–170 (2004).

    Article  Google Scholar 

  6. Mauk, M.D. & Donegan, N. Learn. Mem. 4, 130–158 (1997).

    Article  CAS  Google Scholar 

  7. Rabinovich, M., Huerta, R. & Laurent, G. Science 321, 48–50 (2008).

    Article  CAS  Google Scholar 

  8. Maass, W., Natschläger, T. & Markram, H. Neural Comput. 14, 2531–2560 (2002).

    Article  Google Scholar 

  9. Goldman, M.S. Neuron 61, 621–634 (2009).

    Article  CAS  Google Scholar 

  10. Sompolinsky, H., Crisanti, A. & Sommers, H. Phys. Rev. Lett. 61, 259–262 (1988).

    Article  CAS  Google Scholar 

  11. Sussillo, D. & Abbott, L.F. Neuron 63, 544–557 (2009).

    Article  CAS  Google Scholar 

  12. Haykin, S. Adaptive Filter Theory 4th edn. (Prentice Hall, 2001).

    Google Scholar 

  13. van Vreeswijk, C. & Sompolinsky, H. Science 274, 1724–1726 (1996).

    Article  CAS  Google Scholar 

  14. Monteforte, M. & Wolf, F. Phys. Rev. X 2, 041007 (2012).

    CAS  Google Scholar 

  15. Fujisawa, S., Amarasingham, A., Harrison, M. & Buzsáki, G. Nat. Neurosci. 11, 823–833 (2008).

    Article  CAS  Google Scholar 

Download references

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  1. Alfonso Renart is in the Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.,

    Alfonso Renart

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  1. Alfonso Renart
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Correspondence to Alfonso Renart.

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Renart, A. Recurrent networks learn to tell time. Nat Neurosci 16, 772–774 (2013). https://doi.org/10.1038/nn.3441

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