, 2004). A method was proposed to trace bursting spikes (Pouzat et al., 2004), which can be sorted correctly as bursting spikes of the same neurons. The Markov Chain Monte Carlo algorithm was utilized to estimate the number Compound C clinical trial of source neurons in spike clustering (Nguyen et al., 2003) and to trace a bursting state

(Delescluse & Pouzat, 2006). Spike clustering was solved with the EM method for a mixture model of Student’s t-distributions (Shoham et al., 2003) or with Bayesian inference (Wood & Black, 2008). Spike correlation analysis was shown to require careful treatment of overlapping spikes (Bar-Gad et al., 2001). The detection of submillisecond-range spike coincidences was attempted with massively-parallel multi-channel electrodes and independent-component analysis (Takahashi et al., 2003). Multi-unit data, however, are corrupted by biological

noise and accurate sorting is generally difficult. In particular, the previous methods of spike sorting suffer from convergence to local minima and selection of an inappropriate model (i.e. the number of clusters). The errors left in a computer-aided sorting must be corrected by human eyes but this procedure is time-consuming and inherently suffers from subjective bias (Harris et al., 2000). In the present study, we explore a method for accurate and robust spike sorting to reduce the load of manual operation. We compare several methods of spike sorting by using the data of simultaneous extracellular and intracellular recordings of neuronal activity (Harris et al., 2000; Henze Depsipeptide cell line et al., 2000). These methods include newly

devised methods MycoClean Mycoplasma Removal Kit as well as improved versions of conventional methods. In particular, we developed robust variational Bayes (RVB) for spike clustering and a novel filter for spike detection. Variational Bayes (VB) has been used with a mixture of normal distributions (Attias, 1999), whereas RVB employs a mixture model of Student’s t-distributions. At each stage of spike sorting, we tested known and newly developed mathematical tools, and found that an RVB-based method exhibits an excellent overall sorting performance. All of the sorting methods were solved with deterministic annealing. Neither the EM algorithm nor the variational Bayesian algorithm employs annealing in their usual descriptions. These algorithms, however, are sometimes trapped by local minima that do not correspond to optimal solutions. The deterministic annealing introduces a phenomenological ‘temperature parameter’ to avoid the convergence to non-optimal solutions (Ueda & Nakano, 1998; Katahira et al., 2008). We implemented all of the sorting methods tested in this study into an open-source code named ‘EToS’ (Efficient Technology of Spike sorting) that runs at a high speed. The preliminary results of this study were presented in Takekawa et al. (2008).