Deflation technique for neural spike sorting ...
Document type :
Communication dans un congrès avec actes
Title :
Deflation technique for neural spike sorting in multi-channel recordings
Author(s) :
Tiganj, Zoran [Auteur]
Non-Asymptotic estimation for online systems [NON-A]
Mboup, Mamadou [Auteur]
Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 [CRESTIC]
Non-Asymptotic estimation for online systems [NON-A]
Non-Asymptotic estimation for online systems [NON-A]
Mboup, Mamadou [Auteur]
Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 [CRESTIC]
Non-Asymptotic estimation for online systems [NON-A]
Conference title :
IEEE Workshop on Machine Learning for Signal Processing
City :
Beijing
Country :
Chine
Start date of the conference :
2011-09-18
Publication date :
2011-09-18
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
We propose an ICA based algorithm for spike sorting in multi-channel neural recordings. In such context, the performance of ICA is known to be limited since the number of recording sites is much lower than the number of ...
Show more >We propose an ICA based algorithm for spike sorting in multi-channel neural recordings. In such context, the performance of ICA is known to be limited since the number of recording sites is much lower than the number of the neurons around. The algorithm uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and spike detection for removing the noise and all the spikes that are not coming from the targeted neuron. We validate the performance of the algorithm on simulated data, but also on real simultaneous extracellular-intracellular recordings. The results show that the proposed algorithm performs significantly better than when only ICA is applied.Show less >
Show more >We propose an ICA based algorithm for spike sorting in multi-channel neural recordings. In such context, the performance of ICA is known to be limited since the number of recording sites is much lower than the number of the neurons around. The algorithm uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and spike detection for removing the noise and all the spikes that are not coming from the targeted neuron. We validate the performance of the algorithm on simulated data, but also on real simultaneous extracellular-intracellular recordings. The results show that the proposed algorithm performs significantly better than when only ICA is applied.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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