Abstract
Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend.This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
Highlights
Investigations of fundamental questions in neuroscience, such as the mechanistic basis of behaviour and cognition, generate large volumes of experimental data as well as complex computational models spanning different levels of biological detail
It has been challenging to visualize the data and models required to link the dynamics of the nervous system of Caenorhabditis elegans to its behaviour [1], or to understand how the sleep regulatory circuit in Drosophila melanogaster is affected by the surrounding environment [2]
We have developed Geppetto, an open-source middleware platform for building accessible neuroscience applications
Summary
Investigations of fundamental questions in neuroscience, such as the mechanistic basis of behaviour and cognition, generate large volumes of experimental data as well as complex computational models spanning different levels of biological detail. One popular approach to solving this issue involves using general purpose programming languages such as Python [9,10,11] This approach enables the rapid development of toolchains to solve a specific visualization and integration problem, gluing together multiple libraries and tools [12]. An even greater problem comes from the fact that these tools, and even more so their combination, are rather inaccessible to many researchers Such technological barriers have had a remarkable effect in the neuroscience field as a whole, resulting in modellers and experimentalists working as two different communities separated by a technological divide. This has resulted in computational models that are poorly validated and has left model-generated hypotheses unexplored
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