Linking non-binned spike train kernels to several existing spike train metrics

Linking non-binned spike train kernels to several existing spike train metrics

This work presents two kernels which can be applied to sets of spike times. This allows the use of state-of-the-art classification techniques to spike trains. The presented kernels are closely related to several recent and often used spike train metrics. One of the main advantages is that it does not require the spike trains to be binned. A high temporal resolution is thus preserved which is ne...

Spike train metrics.

Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus, particularly appropriate for extracellularly recorded neural signals. The spike metric approach ...

Spike Train

Monitoring spike train synchrony

Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure t...

Evolving Spike-Train Processors

The research described in this paper was motivated by the idea to process purposefully given spike-trains using a cellular automaton (CA). CAs have three attractive features, namely massive parallelism, locality of cellular interactions, and simplicity of basic components (cells). However, the difficulty of designing a CA for a specific behavior causes limited interest in this computational par...