Measuring spike train distance from multichannel spike trains data simulated by coupled escape rate model

Estimating the population activity patterns between two or more spike trains is a fundamental problem in studying neural coding in computational neuroscience. In recent years, there are many different methods proposed to build a framework to deal with these problems by using spike train metric. Here we suggest a kernel method for multichannel spike trains that can provide an opportunity to measure spike trains. As kernels can be used for various tasks in machine learning, including regression, clustering and dimension reduction. We believe this method is effective at measuring multichannel spike trains simulated using a distance. Keyword spike train distance, coupled escape rate model, kernel methods, multichannel spike trains, neuronal coding