Real-time template matching algorithm for overlapping spikes

We propose a new method to detect and sort overlapping spikes efficiently in real time. This method is based on a neural signal model and sequential Bayesian inference of the firing probability of each neuron. We assessed our method using simulated data demonstrating that our method can robustly extract spike trains from signals including many overlapping spikes.