Deflation technique for neural spike sorting in multi-channel recordings

Zoran Tiganj (1) , Mamadou Mboup (2, 1)

1 NON-A - Non-Asymptotic estimation for online systems
2 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Zoran Tiganj, Mamadou Mboup. Deflation technique for neural spike sorting in multi-channel recordings. IEEE Workshop on Machine Learning for Signal Processing, Sep 2011, Beijing, China. pp.1-6, ⟨10.1109/MLSP.2011.6064619⟩. ⟨hal-00662709⟩