  Background: Spike trains of multiple neurons can be analyzed following the
summed population (SP) or the labeled line (LL) hypothesis. Responses to
external stimuli are generated by a neuronal population as a whole or the
individual neurons have encoding capacities of their own. The SPIKE-distance
estimated either for a single, pooled spike train over a population or for each
neuron separately can serve to quantify these responses.
  New Method: For the SP case we compare three algorithms that search for the
most discriminative subpopulation over all stimulus pairs. For the LL case we
introduce a new algorithm that combines neurons that individually separate
different pairs of stimuli best.
  Results: The best approach for SP is a brute force search over all possible
subpopulations. However, it is only feasible for small populations. For more
realistic settings, simulated annealing clearly outperforms gradient algorithms
with only a limited increase in computational load. Our novel LL approach can
handle very involved coding scenarios despite its computational ease.
  Comparison with Existing Methods: Spike train distances have been extended to
the analysis of neural populations interpolating between SP and LL coding. This
includes parametrizing the importance of distinguishing spikes being fired in
different neurons. Yet, these approaches only consider the population as a
whole. The explicit focus on subpopulations render our algorithms
complimentary.
  Conclusions: The spectrum of encoding possibilities in neural populations is
broad. The SP and LL cases are two extremes for which our algorithms provide
correct identification results.
