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. 2018 Nov 19;12:836. doi: 10.3389/fnins.2018.00836

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Copyright © 2018 Wu, Chua, Zhang, Li and Tan.

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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The effect of spike jittering and spike deletion on the classification accuracy. (A) Classification accuracy as a result of spike jitter added at the input to the SNN classifier. The amount of jitter is added as a fraction of the spike generation period T (i.e., 50 ms used for the RWCP dataset). The classifier is robust to spike jitter, maintaining a high accuracy with different amount of jitter. (B) Classification accuracy as a result of spike deletion at the input to the SNN classifier. The accuracy of the classifier remains stable for spike deletion ratio below 60% and decays with increased spike deletion.