Spike Train

A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing

This letter presents a general framework based on reproducing kernel Hilbert spaces (RKHS) to mathematically describe and manipulate spike trains. The main idea is the definition of inner products to allow spike train signal processing from basic principles while incorporating their statistical description as point processes. Moreover, because many inner products can be formulated, a particular...

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...

Learning Spike Train to Spike Train Transformations in Multilayer Feedforward Neuronal Networks

We address the problem of learning temporally precise spike train to spike train transformations in multilayer feedforward networks of spiking neurons. We pursue a framework based strictly on spike timing, that is, one that avoids invoking concepts pertaining to spike rates. The proposed error functional compares the spike train emitted by the output neuron of the network to the desired spike t...

Likelihood Methods for Neural Spike Train Data Analysis

Frequent Episode Mining and Multi-Neuronal Spike Train Data Analysis

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Multiscale spike train variability in primary electrosensory afferents.

Spike train variability is of fundamental importance for understanding how information is encoded and processed in the nervous system. Most studies in this area have focused on short-term variability, as characterized by the coefficient of variation of the interspike interval distribution. Here we discuss the importance of extending the analysis of spike train variability to longer time scales ...