KOCHEN Sara Silvia
congresos y reuniones científicas
A novel and fully automatic spike sorting implementation with variable number of features
CHAURE F, REY HG, QUIAN QUIROGA R, KOCHEN S..
Congreso; Congreso Anual SAN 2017; 2017
The most widely used spike sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike sorting algorithm that can capture multiple clusters of different size and densities. In addition, we introduce an improved feature selection method by using a variable number of wavelet coefficients based on the degree of non-gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single channel recordings, in about 95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared to previous implementations and also compared to recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real and simulated tetrode recordings.