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eNeuro. 2017 Mar 17;4(2). pii: ENEURO.0022-17.2017. doi: 10.1523/ENEURO.0022-17.2017. eCollection 2017 Mar-Apr.

Neuronify: An Educational Simulator for Neural Circuits.

Author information

1
Centre for Integrative Neuroplasticity, University of Oslo, 0316 Oslo, Norway; Department of Physics, University of Oslo, 0316 Oslo, Norway.
2
Centre for Integrative Neuroplasticity, University of Oslo, 0316 Oslo, Norway; Department of Biosciences, University of Oslo, 0316 Oslo, Norway.
3
Centre for Integrative Neuroplasticity, University of Oslo, 0316 Oslo, Norway; Department of Informatics, University of Oslo, 0316 Oslo, Norway.
4
Centre for Integrative Neuroplasticity, University of Oslo, 0316 Oslo, Norway; Institute of Basic Medical Sciences, University of Oslo, 0316 Oslo, Norway.
5
Centre for Integrative Neuroplasticity, University of Oslo, 0316 Oslo, Norway; Department of Physics, University of Oslo, 0316 Oslo, Norway; Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway.

Abstract

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. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

KEYWORDS:

app; modeling; neural networks; software; teaching

PMID:
28321440
PMCID:
PMC5355897
DOI:
10.1523/ENEURO.0022-17.2017
[Indexed for MEDLINE]
Free PMC Article

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