  One step in the conventional analysis of extracellularly recorded neuronal
data is spike sorting, which separates electrical signal into action potentials
from different neurons. Because spike sorting involves human judgment, it can
be subjective and time intensive, particularly for large sets of neurons. Here
we propose a simple, automated way to construct alternative representations of
neuronal activity, called spectral representation (SR). In this approach,
neuronal spikes are mapped to a discrete space of spike- waveform features and
time. Spectral representation enables us to find single-unit stimulus related
changes in neuronal activity without spike sorting. We tested the ability of
this method to predict stimuli using both simulated data and experimental data
from an auditory mapping study in anesthetized marmoset monkeys. We find that
our approach produces more accurate classification of stimuli than spike sorted
data for both simulated and experimental conditions. Furthermore, this method
lends itself to automated analysis of extracellularly recorded neuronal
ensembles. Additionally, we suggest ways in which these representations can be
readily extended to assist in spike sorting and the evaluation of single-neuron
peri-stimulus time histograms.
