A unified framework and method for automatic neural spike identification

Abstract

Automatic identification of action potentials from one or more extracellular electrode recordings is generally achieved by clustering similar segments of the measured voltage trace, a method that fails (or requires substantial human intervention) for spikes whose waveforms overlap. We formulate the problem in terms of a simple probabilistic model, and develop a unified method to identify spike waveforms along with continuous-valued estimates of their arrival times, even in the presence of overlap. Specifically, we make use of a recent algorithm known as Continuous Basis Pursuit for solving linear inverse problems in which the component occurrences are sparse and are at arbitrary continuous-valued times. We demonstrate significant performance improvements over current state-of-the-art clustering methods for four simulated and two real data sets with ground truth, each of which has previously been used as a benchmark for spike sorting. In addition, performance of our method on each of these data sets surpasses that of the best possible clustering method (i.e., one that is specifically optimized to minimize errors on each data set). Finally, the algorithm is almost completely automated, with a computational cost that scales well for multi-electrode arrays.

A minimax dictionary expansion for sparse continuous reconstruction

“…A simpler approach motivated by the model (9) and the ideal constraint set (14) is possible makingv n = x (1) n and τ n = x (2) n /x (1) n , but we found that (16) and (17) consistently provide smaller errors in our simulations. A possible explanation is the fact that the ideal constraint set is relaxed, as described in Section II-B, allowing solutions that do not strictly follow (14).…”

“…This encourages further investigations on practical problems where the problem of off-grid deviation is present. For instance, the Taylor and the Polar bases have been used, respectively, in neuron spike identification [1] and compressive sensing of frequency-sparse signals [12].…”

“…This formulation is found in many applications of inverse problems such as neuron spike detection [1] and ultrasound nondestructive testing (NDT) [2], [3], where the signal to be reconstructed is modelled as a limited sum of shifted impulses with arbitrary amplitudes.…”

Tetrode Spike Detection Method Based on Quaternion Principle Component Feature Extraction

“…(2) ij=-ji=k (3) jk=-kj=i (4) ki=-ik=j (5) The quaternion can be represented in complex 22  matrices zw a+ib c+id H= = -c+id a-ib -w z       (6) where z and w are complex numbers, a , b , c , and d are real, and z is the complex conjugate of z .…”

“…Yuan Yuan, and Chenhui Yang presented a robust and automatic spike detection and sorting system named the M-Sorter which was equipped with the multiple correlation of wavelet coefficients (MCWC) and a template matching classification [5]. Chaitanya Ekanadham presented a unified frame for automatic identification of neural spike, which used the Continuous Basis Pursuit (CBP) algorithm to classify spikes [6]. Quiroga presented a unsupervised classification method on the wavelet and superparamagnetic clustering [7].…”