Milad Hobbi Mobarhan

• 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
• Martínez-Cañada, Pablo; Mobarhan, Milad; Halnes, Geir; Fyhn, Marianne; Morillas, Christian; Pelayo, Francisco & Einevoll, Gaute (2018). Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. PLoS Computational Biology.  ISSN 1553-734X.  14(1), s 1- 45 . doi: 10.1371/journal.pcbi.1005930 Full text in Research Archive.
• Mobarhan, Milad; Halnes, Geir; Martínez-Cañada, Pablo; Hafting, Torkel; Fyhn, Marianne & Einevoll, Gaute (2018). Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells. PLoS Computational Biology.  ISSN 1553-734X.  14(5) . doi: 10.1371/journal.pcbi.1006156 Full text in Research Archive.
• 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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• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2019). Stable orientation tuning in the freely moving rat: Movement-robust orientation-selective neurons in the deep layers of the primary visual cortex.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2019). Stable orientation tuning in the freely moving rat: Movement-robust orientation-selective neurons in the deep layers of the primary visual cortex.
• Aasebø, Ida Elisabeth Jørgensen; Røe, Malin Benum; Mobarhan, Milad; Lensjø, Kristian Kinden; Stöber, Tristan Manfred; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Orientation selectivity in the freely moving rat: Movement-robust orientation-selectivity (MROS) of units in the deep layers of the rat visual cortex.
• 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.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Movement-resistant orientation selectivity of cells in the deep layers of the rat visual cortex.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Stable orientation tuning in the freely moving rat: Movement-robust orientation selective neurons in the deep layers of the primary visual cortex.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Stöber, Tristan Manfred; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Stable orientation tuning in the freely moving rat: Movement-robust orientation-selective neurons in the deep layers of the primary visual cortex.
• Stasik, Alexander Johannes; Mobarhan, Milad; Hagen, Espen & Einevoll, Gaute (2018). Generating Input from LGN for Simulation of Visual Cortex.
• Aasebø, Ida E. J.; Mobarhan, Milad; Røe, Malin Benum; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2017). Movement-resistant orientation-selective units in the deep layers of the visual cortex.
• 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).

• Mobarhan, Milad; Halnes, Geir; Martínez-Cañada, Pablo; Hafting, Torkel; Fyhn, Marianne & Einevoll, Gaute (2017). Firing-rate Based Network Modeling of the dLGN Circuit: Effects of Cortical Feedback on Spatiotemporal Response Properties of Relay Cells.
• 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.
• 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.
• Mobarhan, Milad; Tennøe, Simen & Solbrå, Andreas Våvang (2016). Neuronify: a new tool for creating simple neural networks.
• Hobbi Mobarhan, Milad; Einevoll, Gaute; Geir, Halnes; Vervaeke, Koen Gerard Alois & Fyhn, Marianne (2015). Roles of cortical feedback in lateral geniculate nucleus.
• Tennøe, Simen; Mobarhan, Milad; Dragly, Svenn-Arne; Solbrå, Andreas Våvang & Langtangen, Hans Petter (2015). CSE: Computing in Science Education.

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