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Svenn-Arne Dragly

Researcher at University of Oslo

Publications -  8
Citations -  1004

Svenn-Arne Dragly is an academic researcher from University of Oslo. The author has contributed to research in topics: Data sharing & Metadata. The author has an hindex of 4, co-authored 8 publications receiving 278 citations.

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Perineuronal nets stabilize the grid cell network

TL;DR: It is shown that removal of perineuronal nets leads to lower inhibitory spiking activity, and reduces grid cells’ ability to create stable representations of a novel environment, and that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons.
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Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai.

TL;DR: An open source, low-cost, adaptable, and easy to set-up system for combined behavioral tracking, electrophysiology, and closed-loop stimulation that robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies
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Experimental Directory Structure (Exdir): An Alternative to HDF5 Without Introducing a New File Format.

TL;DR: This work proposes a novel alternative to Hierarchical Data Format 5, the Experimental Directory Structure (Exdir), an open specification for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages.
Posted ContentDOI

Experimental Directory Structure (Exdir): An alternative to HDF5 without introducing a new file format

TL;DR: The Experimental Directory Structure (Exdir) is a proposal to standardize a storage solution that has become an increasingly popular alternative to Hierarchical Data Format 5, namely to use directories to define a hierarchy, store data in binary files, and metadata in text files.
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Neuronify: An Educational Simulator for Neural Circuits.

TL;DR: An educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment, and allowing students with no programming experience to create and simulate neural networks.