Svenn-Arne Dragly

Doctoral Research Fellow - Condensed Matter Physics

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Email s.a.dragly@fys.uio.no

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Visiting address Sem Sælands vei 24 Fysikkbygningen 0371 OSLO

Postal address Postboks 1048 Blindern 0316 OSLO

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Academic interests

I have a PhD in computational neuroscience from the University of Oslo, where I worked on computational tools and workflows, and the role of perineuronal networks in grid cells.

Teaching

I am involved in the project Computing in Science Education and have been a teaching assistant in the following courses:

FYS1120 - Electromagnetism
FYS-MEK1110 - Mechanics
FYS4411 Computational Physics II: Quantum mechanical systems
FYS4460 - Disordered systems and percolation

Background

My background includes a bachelor in Materials Science and Nanotechnology and a Master’s degree in Computational Physics, both from the University of Oslo. In my Master’s thesis, I wrote about the challenge of simulating large numbers of atoms with high levels of accuracy. To deal with this issue, I showed how detailed calculations in quantum physics can be bridged to large-scale simulations in molecular dynamics, using artificial neural networks.

Christensen, Ane Charlotte; Lepperød, Mikkel; Lensjø, Kristian Kinden; Dragly, Svenn-Arne; Blackstad, Jan Sigurd Beddari; Fyhn, Marianne & Hafting, Torkel (2019). Perineuronal nets stabilize the grid cell network. bioRxiv - the preprint server for biology. . doi: 10.1101/796342
Buccino, Alessio Paolo; Lepperød, Mikkel Elle; Dragly, Svenn-Arne; Häfliger, Philipp; Fyhn, Marianne & Hafting, Torkel (2018). Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai. Journal of Neural Engineering. ISSN 1741-2560. 15, s 1- 12 . doi: 10.1088/1741-2552/aacf45 Full text in Research Archive.
Dragly, Svenn-Arne; Mobarhan, Milad; Lepperød, Mikkel Elle; Tennøe, Simen; Fyhn, Marianne; Hafting, Torkel & Malthe-Sørenssen, Anders (2018). Experimental directory structure (Exdir): An alternative to HDF5 without introducing a new file format. Frontiers in Neuroinformatics. ISSN 1662-5196. 12 . doi: 10.3389/fninf.2018.00016
Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An educational simulator for neural circuits. eNeuro. ISSN 2373-2822. 4(2:e0022-17.2017), s 1- 13 . doi: 10.1523/ENEURO.0022-17.2017 Full text in Research Archive.

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Dragly, Svenn-Arne (2019). Computational Tools for Integrative Neuroscience. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. 2121. Full text in Research Archive.
Dragly, Svenn-Arne; Mobarhan, Milad; Lepperød, Mikkel Elle; Tennøe, Simen; Stasik, Alexander Johannes; Fyhn, Marianne; Hafting, Torkel & Malthe-Sørenssen, Anders (2018). Exdir An alternative to HDF5 that uses Numpy files for data, YAML for metadata and simple directories to define the hierarchy.
Hafreager, Anders; Dragly, Svenn-Arne & Malthe-Sørenssen, Anders (2018). Increased productivity with real time atomistic simulations using Atomify. Show summary
Increased productivity with real time atomistic simulations using Atomify
Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An Educational Simulator for Neural Circuits. Show summary
Neurons are cells in the brain that are able to rapidly change the electric field across their cell membrane. These changes allow neurons to communicate with each other and is the basis for the complex computations in the brain. Understanding how neurons communicate and the properties of neuronal networks is essential for neuroscience students. Traditionally, students draw networks with pen and paper and qualitatively deduce features of the network by analyzing the static drawings. Here, we present Neuronify, an app that allows students to draw the same networks on a computer or mobile device and run dynamic simulations without programming. The students can test their analysis by running the network and check their predictions against the outcome. Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. Such software is readily available in many areas of natural science such as physics and electrical engineering. However, few educational apps are available for simulation of neural networks. Neuronify allows the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can drag and drop network elements such as neurons, electrical stimulation tools and recording devices. The components can then easily be connected to one another. Building intuition for how neurons and neural networks behave has been a top priority in designing Neuronify. We aim to provide a low entry point to simulation-based neuroscience. Most undergraduate students do not have the computational experience to create their own neural simulator. Neuronify offers them an opportunity to build and experiment with neural networks in a graphical and easy-to-understand interface. By playing around with the networks, the students can develop a good understanding of their properties. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt. It has been downloaded more than 30,000 times since its launch and is available on smart phones (Android, iOS), tablet computers as well personal computers (Windows, Mac, Linux).
Tennøe, Simen; Mobarhan, Milad; Dragly, Svenn-Arne; Solbrå, Andreas Våvang & Nederbragt, Alexander Johan (2017). Teaching modelling to first-year biology students. Show summary
The field of biology relies heavily on computations. This is not well reflected in education and the current undergraduate curriculum has little computational content. This results in a discontinuity between the education received by the students and the problems they face after graduation. The end result is that the students are not equipped to meet the requirements of modern research nor the tasks awaiting them in the industry. To remedy this problem, a new course, BIOS 1100 - Introduction to Modelling in Biology, will be held for the first time at the University of Oslo in fall 2017. Currently, no available book combines biology and programming at an introductory level in a satisfactory way. Most textbooks teach both topics separately or expect the reader to know either biology or programming from before. We have therefore written our own textbook to be used as curriculum in this course. This book aims to teach programming and modelling to first year biology students through examples from biology. The book is based on a philosophy of just-in-time teaching where the programming concepts are introduced just when they are needed to solve the problem in hand. This puts the programming content in an unusual order in comparison to the traditional computer science curriculum while keeping the biology students motivated by the problems they solve. The examples are mainly from the three branches of biology: population dynamics, bioinformatics and evolution. The purpose is to give biology students a practical understanding of programming and mathematical models. This will enable the understanding of mathematical models and encourage critical thinking. Programming allows much more realistic and inspiring problems to be addressed, enabling students to work on current research topics early on. The textbook is written using DocOnce (created by Hans Petter Langtangen), which enables us to compile the book to both LaTeX/PDF, HTML and Jupyter Notebooks.
Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang & Tennøe, Simen (2016). Neuronify: An educational app for simulation of neural circuits.
Hafreager, Anders & Dragly, Svenn-Arne (2016). Atomify - Editing, simulation and visualization in one tool.
Lepperød, Mikkel Elle; Wigestrand, Mattis B; Lensjø, Kristian Kinden; Frey, M.M.; Dragly, Svenn-Arne; Einevoll, Gaute; Solstad, Trygve; Fyhn, Marianne & Hafting, Torkel (2016). Grid cell network resilient to input-specific medial septum perturbations.
Mobarhan, Milad; Dragly, Svenn-Arne & Solbrå, Andreas Våvang (2016). Neuronify: an educational app for simulation of neural circuits.
Mobarhan, Milad; Dragly, Svenn-Arne; Tennøe, Simen & Solbrå, Andreas Våvang (2016). Introduction to Computational Biology with Python.
Dragly, Svenn-Arne; Ness, Torbjørn Vefferstad; Vervaeke, Koen Gerard Alois & Einevoll, Gaute (2015). Discovering extracellular signatures by modeling of local field potentials.
Hafreager, Anders & Dragly, Svenn-Arne (2015). Nerver, hjernen og teknologi: Forstå hjernen med programmering.
Tennøe, Simen; Mobarhan, Milad; Dragly, Svenn-Arne; Solbrå, Andreas Våvang & Langtangen, Hans Petter (2015). CSE: Computing in Science Education.

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Published Oct. 9, 2014 12:31 PM - Last modified Nov. 20, 2019 5:44 PM

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Neurobiology - Fyhn

Svenn-Arne Dragly

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