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. 2022 Apr 28;16:858377. doi: 10.3389/fnins.2022.858377

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Copyright © 2022 Vasko, Aume, Tamrakar, Colachis, Dunlap, Rich, Meyers, Gabrieli and Friedenberg.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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(A) The mean accuracy for the three uNN decoders, replotted from Figure 3B with x-axis ticks realigned to match (B). (B) The computational requirements of the uNN decoders as measured by the number of batches required for training after disruptions are introduced. When zero channels are affected, no masking or retraining takes place, and the model would only receive unsupervised updates. The number of batches required is according to an early stopping criterion and is averaged over each of the 10 random initializations applied. Error bars represent 95% confidence intervals. (C) The number of batches required as a function of decoder accuracy.