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Recurrent networks learn to tell time

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.


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Correspondence to Alfonso Renart.

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The author declares no competing financial interests.

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Renart, A. Recurrent networks learn to tell time. Nat Neurosci 16, 772–774 (2013).

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