<p>Cilj: Brzina ispaljivanja neuralnih impulsa u kori velikog mozga je veoma promenljiva što ukazuje da bi Poasonov tačkasti proces mogao da bude pogodan za modelirqnje takvog procesa. Međutim, brojna istraživanja su pokazala da statistika ispaljivanja ne sledi Poasona. Uprkos tome, još uvek se nije iskristalisao ni alternativni mehanizam koji bi opisao generisanje spajkova, ni raspodela koja bi opisala raspodelu intervala između spajkova (ISI). Ključni cilj ove disertacije je statistička analiza koja će omogućiti modelovanje ISI vremenskih nizova snimljenih u različitim delovima kore velikog mozga dok su majmuni rešavali različite probleme.<br />Metoda: Primenjena je robusna neparametarska statistika da bi se odredila funkcija gustine raspodele (PDF) ISI vremenskih nizova. Rezultati su verifikovani butstrep metodom i iskorišćeni za kreiranje Markovljevog modela.<br />Rezultati: Pokazalo se da se raspodela ISI intervala ne može opisati samo jednom funkcijom i da se statistika ne može da poveže isključivo sa već postojećim modelima, uključujući i eksponencijalni. Pokazalo se, zatim, da ISI statistika ne zavisi od regije u kori velikog mozga, niti, unutar jedne regije, od problema koji je budni majmun rešavao. Međutim, ISI mizovi snimani dok je majmun rešavao isti problem ali u različitim vremenskim intervalima nisu statistički slični, što ukazuje na postojanje varijabiliteta u ISI vremenskim nizovima u zavisnosti od problema koji se rešava.<br />Zaključak: Rezultati analize signala ukazuju da je neuralna aktivnost posledica komplesnih generišućih mehanizama sa značajnom međuzavisnošću i da process zavisi od zadatka koji se rešava.</p> / <p>Objectives: High variability of neuronal firing patterns in the cerebral cortex points towards spiking activity models based on Poisson point processes. In spite of growing evidence that firing behavior may fail Poisson statistics, an alternate spike generating mechanisms and the resulting inter-spike interval (ISI) distributions have not been clarified yet. The key objective of this thesis is to perform a statistical analysis that would yield a model of ISI time series recorded from different from different cortical areas of awake monkeys performing various behavioral tasks.<br />Methods: A robust and non-parametrical statistics to determine ISI probability density functions (PDF-s) of extracellularly recorded cerebral cortical neurons of behaving macaque monkeys is performed. The results were validated using the bootstrap method. The obtained statistics were used to create a Markov model of ISI time series.<br />Results: It turned out that there is no single ISI distribution, but many, and that the underlying statistics is not associated exclusively to the current established models including the exponential. Distribution of types of ISI statistics obtained from different cortical areas are statistically similar and the same applies to the statistics obtained from the same cortical area by ignoring ongoing behavior. However, particular ISI time series observed during the time epochs of the same behavioral task did not show statistical similarity, suggesting a task dependent variation of spike generating dynamics.<br />Conclusion: In summary, the results indicate that neuronal firing activity is resulted by complex generative mechanisms with significant dependency and that this process is contingent upon the behavior.</p>
| Identifer | oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)107581 |
| Date | 11 September 2018 |
| Creators | Minich Janoš |
| Contributors | Bajić Dragana, Négyessy László, Fülöp Bazsó, Šenk Vojin, Delić Vlado |
| Publisher | Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, University of Novi Sad, Faculty of Technical Sciences at Novi Sad |
| Source Sets | University of Novi Sad |
| Language | English |
| Detected Language | Unknown |
| Type | PhD thesis |
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