# Enhanced neural discrimination of sensory stimuli using an adaptive
spike distance

**Source**: https://www.maths.tcd.ie/report_series/abstracts/tcdm0714.html
**Parent**: https://www.maths.tcd.ie/research/papers/

**Enhanced neural discrimination of sensory stimuli using an adaptive
spike distance**

It was once common to believe that the timing of spikes in spike
trains was too variable to carry information and that in the sensory
pathways and that information about stimuli was represented by spike
rates. While it is now thought possible that stimuli are also
represented in spike-timing features, it is still not fully understood
how to describe the variability and coding function of these features.
One approach is to define a spike train metric, that is, a measure of
the distance between two spike trains. A good metric will measure a
short distance between responses to the same input and a longer
distance between responses to different inputs and can be used to
quantify the significance of variability between putative timing
features. Here, we define a new metric. It is constructed using a
non-linear transformation of spike trains into functions and is
motivated physiologically by a simple model of synaptic conductance
which takes adaptation into account. This metric proves effective at
classifying neuronal responses by stimuli in the sample data set of
electro-physiological recordings from the field L auditory area of the
zebra finch fore-brain.