RT Journal Article
SR Electronic
T1 Neuronify: An Educational Simulator for Neural Circuits
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0022-17.2017
DO 10.1523/ENEURO.0022-17.2017
A1 Svenn-Arne Dragly
A1 Milad Hobbi Mobarhan
A1 Andreas Våvang Solbrå
A1 Simen Tennøe
A1 Anders Hafreager
A1 Anders Malthe-Sørenssen
A1 Marianne Fyhn
A1 Torkel Hafting
A1 Gaute T. Einevoll
YR 2017
UL http://www.eneuro.org/content/early/2017/03/09/ENEURO.0022-17.2017.abstract
AB 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 loud speaker). 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), tablet computers as well personal computers (Windows, Mac, Linux).Significance Statement Neuronify, a new educational software application (app) providing an interactive way of learning about neural networks, is described. Neuronify allows students with no programming experience to easily build and explore networks in a plug-and-play manner picking network elements (neurons, stimulators, recording devices) from a menu. The app is based on the commonly used integrate-and-fire type model neuron and has adjustable neuronal and synaptic parameters. To facilitate teaching, Neuronify comes with premade network motifs performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify will be available for smart phones (Android, iOS), tablet computers as well personal computers (Windows, Mac, Linux).