Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
- 1Laboratoire d'Électronique et de Technologie de l'Information (LETI), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Grenoble, France
- 2Université Grenoble Alpes, Grenoble, France
- 3Laboratoire d'Intégration de Systèmes et de Technologies (LIST), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Gif-sur-Yvette, France
- 4Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, Université de Bordeaux, CNRS, Bordeaux, France
- 5BrainTech Laboratory U1205, Institut National de la Santé et de la Recherche Médicale, Grenoble, France
- 6BrainTech Laboratory U1205, Université Grenoble Alpes, Grenoble, France
by Werner, T., Vianello, E., Bichler, O., Garbin, D., Cattaert, D., Yvert, B., et al. (2016). Front. Neurosci. 10:474. doi: 10.3389/fnins.2016.00474
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.
Conflict of Interest Statement
Keywords: brain-computer interfaces, neuromorphic computing, OxRAM, resistive RAM (RRAM) synapse, spike sorting, spiking neural network, spike timing-dependent plasticity
Citation: Werner T, Vianello E, Bichler O, Garbin D, Cattaert D, Yvert B, De Salvo B and Perniola L (2017) Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting. Front. Neurosci. 11:486. doi: 10.3389/fnins.2017.00486
Received: 04 July 2017; Accepted: 16 August 2017;
Published: 29 August 2017.
Edited by:Calogero Maria Oddo, Sant'Anna School of Advanced Studies, Italy
Reviewed by:Horacio Rostro Gonzalez, Universidad de Guanajuato, Mexico
Doo Seok Jeong, Korea Institute of Science and Technology, South Korea
Copyright © 2017 Werner, Vianello, Bichler, Garbin, Cattaert, Yvert, De Salvo and Perniola. 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) or licensor 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.