Optimizing the learning rate for adaptive estimation of neural encoding models
Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model...
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Veröffentlicht in: | PLoS computational biology 2018-05, Vol.14 (5), p.e1006168-e1006168 |
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