# Studying spike trains using a van Rossum metric  with a synapse-like filter

**Source**: https://www.maths.tcd.ie/report_series/abstracts/tcdm0807.html
**Parent**: https://www.maths.tcd.ie/research/papers/

**Studying spike trains using a van Rossum metric with a synapse-like filter**

Spike trains are unreliable. For example, in the primary
sensory areas, spike patterns and precise spike times will vary
between responses to the same stimulus. Nonetheless, information about
sensory inputs is communicated in the form of spike trains. A
challenge in understanding spike trains is to assess the significance
of individual spikes in encoding information. One approach is to
define a spike train metric, allowing a distance to be calculated
between pairs of spike trains. In a good metric, this distance will
depend on the information the spike trains encode. This method has
been used previously to calculate the timescale over which the
precision of spike times is significant. Here, a new metric is
constructed based on a simple model of synaptic conductances which
includes binding site depletion. Including binding site depletion in
the metric means that a given individual spike has a smaller effect
on the distance if it occurs soon after other spikes. The metric
proves effective at classifying neuronal responses by stimuli in the
sample data set of electro-physiological recordings from the primary
auditory area of the zebra finch fore-brain. This shows that this is
an effective metric for these spike trains suggesting that in these
spike trains the significance of a spike is modulated by its
proximity to previous spikes. This modulation is a putative
information-coding property of spike trains.