TY - JOUR T1 - Neuronify: An Educational Simulator for Neural Circuits JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0022-17.2017 SP - ENEURO.0022-17.2017 AU - Svenn-Arne Dragly AU - Milad Hobbi Mobarhan AU - Andreas Våvang Solbrå AU - Simen Tennøe AU - Anders Hafreager AU - Anders Malthe-Sørenssen AU - Marianne Fyhn AU - Torkel Hafting AU - Gaute T. Einevoll Y1 - 2017/03/09 UR - http://www.eneuro.org/content/early/2017/03/09/ENEURO.0022-17.2017.abstract N2 - 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). ER -