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ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing
Sergey G Aleksin1, Kaiyu Zheng2, Dmitri A Rusakov2
1AMC Bridge LLC, Waltham MA, United States of America and Dnipro, Ukraine.
Plos Computational Biology
|April 1, 2017
Summary
ARACHNE is a new simulation environment that allows neuroscientists to build and explore complex neural networks. This tool simplifies the process, making realistic neural network modeling accessible to researchers without extensive programming expertise.
Area of Science:
- Computational Neuroscience
- Neuroscience Software Development
Background:
- Realistic neural network modeling traditionally requires significant computational resources and programming expertise, limiting accessibility for experimental neuroscientists.
- Existing tools often present a steep learning curve, hindering the integration of computational modeling into experimental workflows.
Purpose of the Study:
- To introduce ARACHNE, a novel simulation environment designed to simplify the creation and exploration of complex biophysical and architectural neural networks.
- To enable experimental neuroscientists to build and investigate cellular networks on local or mobile devices, overcoming previous computational barriers.
Main Methods:
- ARACHNE utilizes the NEURON simulation logic through a user-friendly interface, accessible on various devices.
- It integrates an optimized computational kernel on remote computer clusters, controlled via the internet for demanding simulations.
- The environment supports the combination of neuronal (wired) and astroglial (extracellular volume-transmission driven) network types, incorporating realistic cell models from the NEURON library.
Main Results:
- ARACHNE provides an accessible platform for building and exploring complex neural networks with arbitrary biophysical and architectural properties.
- The environment successfully integrates neuronal and astroglial network components, facilitating the study of diverse neural system dynamics.
- The system allows for control of remote computational resources through a simple interface, democratizing access to high-performance computing for neuroscience research.
Conclusions:
- ARACHNE significantly lowers the barrier to entry for realistic neural network simulations, empowering experimental neuroscientists.
- This simulation environment facilitates the investigation of complex neural systems by combining different cell types and realistic biophysical models.
- ARACHNE is available as open-source software, promoting wider adoption and further development in the neuroscience community.
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