Using spike train distances to identify the most discriminative neuronal subpopulation

TL;DR

This paper introduces new algorithms to identify the most discriminative neuronal subpopulations using spike train distances, effectively distinguishing between population and individual neuron encoding in neural responses.

Contribution

The paper presents novel algorithms for selecting discriminative neuronal subpopulations under both population and labeled line hypotheses, improving analysis of neural encoding.

Findings

Brute force search is optimal for small populations in the SP case.

Simulated annealing outperforms gradient algorithms for larger populations.

The LL algorithm efficiently handles complex coding scenarios.

Abstract

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.