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master's thesis
Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja
Zagreb: University of Zagreb, Faculty of Science, 2025. urn:nbn:hr:217:479192
Pavlović, Bartol
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Pavlović, B. (2025). Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:479192

Pavlović, Bartol. "Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2025. https://urn.nsk.hr/urn:nbn:hr:217:479192

Pavlović, Bartol. "Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2025. https://urn.nsk.hr/urn:nbn:hr:217:479192

Pavlović, B. (2025). 'Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja', Master's thesis, University of Zagreb, Faculty of Science, accessed 26 March 2025, https://urn.nsk.hr/urn:nbn:hr:217:479192

Pavlović B. Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2025 [cited 2025 March 26] Available at: https://urn.nsk.hr/urn:nbn:hr:217:479192

B. Pavlović, "Određivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2025. Available at: https://urn.nsk.hr/urn:nbn:hr:217:479192

Metadata
TitleOdređivanje dinamike fizikalnog sustava pomoću interpretabilnih algoritama dubokog učenja
Title (english)Determining the dynamics of a physical system using interpretable deep learning algorithms
AuthorBartol Pavlović
MentorDavor Horvatić (mentor)
Committee memberDavor Horvatić (predsjednik povjerenstva)
Committee memberMatko Glunčić (član povjerenstva)
Committee memberPetar Žugec (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2025-01-20, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Ovaj diplomski rad istražuje primjenu interpretabilnih algoritama dubokog učenja za modeliranje dinamike populacije neurona koristeći podatke vremenskih nizova. Neuronske simulacije generirane Brian 2 simulatorom služe kao primarni skup podataka, replicirajući ponašanje međusobno povezane mreže neurona u kontroliranim uvjetima. U istraživanju se primjenjuje novi duboki konvolucijski okvir osmišljen za multivarijatne vremenske nizove, s naglaskom na interpretabilnost bez gubitka točnosti... More predikcije. To se postiže arhitekturom sposobnom za hvatanje aktivnosti pojedinačnih neurona i kolektivne dinamike, kao i pružanje uvida u temeljne mehanizme koji upravljaju tim ponašanjima. Prepoznavanjem najbitnijih značajki filtriranjem signala od šuma i aproksimacijom interakcija zadanim funkcijama, model spaja fleksibilnost dubokog učenja i interpretabilnost klasičnih regresija. Fokusirajući se na Izhikievichev model neurona, rad istražuje sposobnost modeliranja sustava vremenskih nizova i interpretabilnost modela. Rezultati pokazuju da ovakav interpretabilni pristup dubokog učenja može učinkovito analizirati i predvidjeti neuronsku dinamiku, povezujući područja strojnog učenja, neuroznanosti i fizike. Također, razvijene su nove ideje za prilagođavanje izlaza modela promatranom sustavu. Less
Abstract (english)
This thesis explores the application of interpretable deep learning algorithms for modeling the dynamics of neuron populations using time-series data. Neural simulations generated with the Brian 2 simulator serve as the primary dataset, replicating the behavior of interconnected neural networks under controlled conditions. The study employs a novel deep convolutional framework for multivariate time series, emphasizing interpretability without sacrificing predictive accuracy. This is... More achieved through an architecture capable of capturing the activity of individual neurons and collective dynamics while providing insights into the fundamental mechanisms governing these behaviors. By identifying key features, filtering signals from noise, and approximating interactions with predefined functions, the model combines the flexibility of deep learning with the interpretability of classical regression techniques. Focusing on the Izhikevich neuron model, the work examines the capacity of time-series systems to model and interpret the dynamics. The results demonstrate that such an interpretable deep learning approach can effectively analyze and predict neural dynamics, bridging the fields of machine learning, neuroscience, and physics. Additionally, new ideas were developed for adapting the model's outputs to the observed system. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:479192
Study programmeTitle: Physics; specializations in: Research
Course: Research
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: magistar/magistra fizike (magistar/magistra fizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2025-01-17 10:01:48
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