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Data
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Unsupervised learning of an efficient short-term memory network (Vertechi, Brendel & Machens 2014)
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Learning in recurrent neural networks has been a topic fraught with
difficulties and problems. We here report substantial progress in the
unsupervised learning of recurrent networks that can keep track
of an input signal. Specifically, we show how these networks can
learn to efficiently represent their present and past inputs, based
on local learning rules only.
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Vertechi P, Brendel W, Machens CK (2014) Show
Other
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Brendel, Wieland [wieland.brendel at bethgelab.org] Show
Other
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wieland.brendel@bethgelab.org
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Brendel, Wieland <wieland.brendel@bethgelab.org>
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Unsupervised learning of an efficient short-term memory network, Vertechi*, P and Brendel*, W and Machens C. K., Advances in Neural Information Processing Systems 27, 2014
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