Nina Purg

Nina Purg

Dec
06
2022

R package for the construction and evaluation of linear models for the analysis of task-related fMRI data

We published a paper in Frontiers in Neuroimaging with a title autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data. The paper presents an original package for the R programming language that allows the evaluation of linear models and the automatic calculation of

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Dec
06
2022

R paket za pripravo in evalvacijo linearnih modelov pri analizi fMR podatkov med izvedbo kognitivnih nalog

V Frontiers in Neuroimaging smo objavili članek z naslovom autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data. Članek predstavlja izviren paket orodij za programski jezik R, ki omogoča evalvacijo linearnih modelov in avtomatični izračun prediktorjev v modelu na podlagi izmerjenih fMR podatkov.

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Nov
24
2022

Grega Repovš o naših refleksih v oddaji Ugriznimo znanost

Vodja laboratorija, prof. dr. Grega Repovš, ja nastopil v oddaji Ugriznimo znanost na RTVSLO ter pojasnil namen in delovanje naših refleksov. Celoten posnetek oddaje je dostopen v spletnem arhivu RTVSLO. Vabljeni k ogledu!‌ ‌

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Nov
14
2022

Pregledni članek na temo globokega učenja v analizi nevroslikovnih podatkov

V Frontiers in Neuroimaging je izšel članek z naslovom Deep learning in neuroimaging data analysis: Applications, challenges, and solutions pod avtorstvom Leva Kiara Avberška in Grege Repovša. Članek podaja pregled uporabe globokega učenja kot oblike strojnega učenja v analizi nevroslikovnih podatkov. Članek je dostopen na spletni strani revije Frontiers in

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Nov
14
2022

A review paper on deep learning in neuroimaging data analysis

The paper entitled Deep learning in neuroimaging data analysis: Applications, challenges, and solutions by Lev Kiar Avberšek and Grega Repovš has been published in Frontiers in Neuroimaging. The paper provides an overview of the use of deep learning as a form of machine learning in the analysis of neuroimaging data.

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Sep
26
2022

Tutorial: Finding and Assigning Peaks in Task-Related fMRI Analysis using Qunex

Brain activity data inferred from functional MR images (fMRI) are stored in a huge voxel space when acquired, resulting in heavy computational operations and cumbersome data analysis, if no other data reduction approaches are applied. The first common approach to data reduction is to project cortical brain activity onto a

Jul
18
2022

Publication of The Cambridge Handbook of Working Memory and Language

The Cambridge University Press has recently published the book The Cambridge Handbook of Working Memory and Language, which includes the chapter The Cognitive Neuroscience of Working Memory and Language written by Nina Purg, Anka Slana Ozimič and Grega Repovš. The book is a result of the collaboration of many established

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Jul
18
2022

Objava knjige The Cambridge Handbook of Working Memory and Language

Pred kratkim je v okviru založbe Cambridge University Press izšla knjiga The Cambridge Handbook of Working Memory and Language, v kateri so Nina Purg, Anka Slana Ozimič in Grega Repovš prispevali sedmo poglavje z naslovom The Cognitive Neuroscience of Working Memory and Language. Knjiga je rezultat sodelovanja številnih uveljavljenih akademikov

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May
18
2022

Nov članek na temo fenomenologije vidno-prostorskega delovnega spomina

Objavili smo članek v Frontiers in Psychology z naslovom "What Individuals Experience During Visuo-Spatial Working Memory Task Performance: An Exploratory Phenomenological Study". Članek je dostopen na spletnih straneh Frontiers in Psychology [https://doi.org/10.3389/fpsyg.2022.811712].

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May
18
2022

New paper on the phenomenology of visuo-spatial working memory

Our paper "What Individuals Experience During Visuo-Spatial Working Memory Task Performance: An Exploratory Phenomenological Study" has been published in Frontiers in Psychology. The paper is available online at the Frontiers in Psychology website [https://doi.org/10.3389/fpsyg.2022.811712].

1 min read