Connectionists: Using spike train distances to identify the most discriminative neuronal subpopulation

[Thomas Kreuz]

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 at cnr.it
Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/


-- 
Institute for complex systems, CNR
Via Madonna del Piano 10
50119 Sesto Fiorentino (Italy)
Tel: +39-349-0748506
Email: thomas.kreuz at cnr.it
Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/
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