Book/Dissertation / PhD Thesis

FZJ-2022-03140

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Higher-order correlation analysis in massively parallel recordings in behaving monkey

Stella, A. (Corresponding author)FZJ*

2022
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich
ISBN: 978-3-95806-640-3

Please use a persistent id in citations: http://hdl.handle.net/2128/31804  urn:nbn:de:0001-2022090718

Abstract: It has been hypothesized that information processing in the cortical network evolves through the subsequent activation of groups of neurons called cell assemblies, and correlated activity is thought to be the signature of their activation. Numerous studies have assessed the presence of precisely-timed spatio-temporal spike patterns (STPs), defined here as sequences of spikes emitted by a set of neurons with fixed time delays between the spikes, repeating in the same configuration in all occurrences. SPADE (Spatio-temporal PAttern Detection and Evaluation) was introduced as an analysis method for the detection of synchronous patterns, and then extended for the detection of spike patterns with temporal delays in parallel spike trains. However, the method was evaluated for the STP detection on simple artificial data, and not yet applied on experimental spike trains. In this thesis we introduce an extension of the original statistical test of SPADE, accounting for the temporal duration of the patterns, the order of correlation and the frequency of pattern occurrence. In this way, we assess that statistical performances are strongly improved. Additionally, we propose an optimized implementation of the mining algorithm of SPADE. We test the implementation on a wide range of different hardware and on real experimental data, showing that it results to be between one and two orders of magnitude faster andmore memory efficient. We also propose five artificial data sets, reproducing with increasing degree the statistical complexity of experimental data, still being completely artificial and generated by point process models. Such data sets may be employed as ground truth for analysis methods of parallel spike trains. Furthermore, we compare different surrogate techniques to evaluate their effect on parallel spike trains statistics and on the evaluation of STP significance. Our results show that the most classical method of uniform dithering fails as an appropriate surrogate, since it leads to underestimation of significance. Thus, we propose an alternative method with better performance. Finally, we analyze with SPADE experimental data from the neural activity recorded from the motor cortex of two macaque monkeys, trained to execute a reaching-and-grasping task. We find that significant STPs occur in all phases of the behavior, and are highly specific to the behavioral context, suggesting that different cell assemblies are active in the context of different behaviors. Moreover, our analysis reveals neurons that are involved in several patterns in differentbehavioral contexts, and are not clustered in space

Note: Dissertation, RWTH Aachen University, 2022

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Appears in the scientific report 2022

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