Corrigendum: Spiking neural networks based on OxRAM synapses for real-time unsupervised spike sorting. [Front. Neurosci, 10, (2016) (474)] DOI: 10.3389/fnins.2016.00474

Abstract

The way we presented the results in the original article may suggest that the proposed spike-sorting approach managed to achieve an accuracy of 90% classification, while, as it can be inferred from the study, this referred to a detection rate not accounting for false positives. We would thus like to make the results clearer by modifying the text as follows: The end of the Abstract should read: This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal without any supervision. The end of the second paragraph of the -Spike Sorting Performance of SNN Application- Section on page 9 should read: As shown in Figure 13, the system reached its mean spike recognition rate of 85.5% after 15 s (corresponding to 50 Spike A events), calculated starting from the first occurrence of Spike A in the ES signal at (t = 285 s), with a false positive rate of 6.9%. The -Spike Sorting Performance of SNN Application- Section paragraph at the beginning of page 10 should read: Without changing the parameters of our filter bank and SNN, the recognition rate for CF2 is 74.2 and 82.1% for B1. This still high detection rate was however accompanied by a poorer classification accuracy with a high number of false positives (274% for CF2 comprising many overlapping waveforms and 61% for B1 displaying a lower signal-to-noise ratio, as compared to 6.9% for CF1), suggesting that further efforts remain to be put to improve the proposed approach to make it robust in all cases.