Neuronify is an intuitive app if you want to test small network simulations and learn about neuroscience. 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.
Download the app for iphone, android or pc: http://cinpla.org/neuronify/
The app is based on a classical model computational scientist have been using to investigate integrate neural circuits. 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). Read more about the science behind the app in the paper:
Read the paper at: http://eneuro.org/content/early/2017/03/09/ENEURO.0022-17.2017
Title: Neuronify: An Educational Simulator for Neural Circuits
Authors: Svenn-Arne Dragly, Milad Hobbi Mobarhan, Andreas Våvang Solbrå, Simen Tennøe, Anders Hafreager, Anders Malthe-Sørenssen, Marianne Fyhn, Torkel Hafting and Gaute T. Einevoll
Source: ENEURO.0022-17.2017; DOI: https://doi.org/10.1523/ENEURO.0022-17.2017
Read more about it here.
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 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).