Dear all, may I kindly draw your attention to our most recent paper which contains several new algorithms to address neuronal population coding using spike train distances: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). For the abstract see below. This paper is part of the dissertation "Spike train distances and neuronal coding <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?sequence=4>" of my PhD student Eero Satuvuori whose full thesis can now be found here <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?sequence=4>. Besides some original parts and the paper cited above it also contains these recent works: Satuvuori E, Kreuz T: Which spike train distance is most suitable for distinguishing rate and temporal coding? JNeurosci Methods 299, 22 [PDF <https://ac.els-cdn.com/S0165027018300372/1-s2.0-S0165027018300372-main.pdf?_tid=spdf-55c0f954-726f-4956-8fa2-7c74fc998aac&acdnat=1519858583_7f3ae963c1d55b2a063399b53f8b1a4e>] and arXiv [PDF <https://arxiv.org/pdf/1708.07508.pdf>] (2018). Kreuz T, Satuvuori E, Pofahl M, Mulansky M: Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns New J. Phys., 19, 043028 [PDF <https://doi.org/10.1088/1367-2630/aa68c3>] and arXiv [PDF <https://arxiv.org/pdf/1610.07986v4.pdf>] (2017). Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T: Measures of spike train synchrony for data with multiple time-scales JNeurosci Methods 287, 25 [PDF <https://doi.org/10.1016/j.jneumeth.2017.05.028>] and arXiv [PDF <https://arxiv.org/pdf/1702.05394.pdf>] (2017). All the best, Thomas Kreuz PS: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). 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. -- Institute for complex systems, CNR Via Madonna del Piano 10 50119 Sesto Fiorentino (Italy) Tel: +39-349-0748506 Email: thomas.kreuz@cnr.it Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/