Decoupling model descriptions from execution: a modular paradigm for extensible neurosimulation with EDEN.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2025-08-07 eCollection Date: 2025-01-01 DOI:10.3389/fninf.2025.1572782
Sotirios Panagiotou, Rene Miedema, Dimitrios Soudris, Christos Strydis
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Abstract

Computational-neuroscience simulators have traditionally been constrained by tightly coupled simulation engines and modeling languages, limiting their flexibility and scalability. Retrofitting these platforms to accommodate new backends is often costly, and sharing models across simulators remains cumbersome. This paper puts forward an alternative approach based on the EDEN neural simulator, which introduces a modular stack that decouples abstract model descriptions from execution. This architecture enhances flexibility and extensibility by enabling seamless integration of multiple backends, including hardware accelerators, without extensive reprogramming. Through the use of NeuroML, simulation developers can focus on high-performance execution, while model users benefit from improved portability without the need to implement custom simulation engines. Additionally, the proposed method for incorporating arbitrary simulation platforms-from model-optimized code kernels to custom hardware devices-as backends offers a more sustainable and adaptable framework for the computational-neuroscience community. The effectiveness of EDEN's approach is demonstrated by integrating two distinct backends: flexHH, an FPGA-based accelerator for extended Hodgkin-Huxley networks, and SpiNNaker, the well-known, neuromorphic platform for large-scale spiking neural networks. Experimental results show that EDEN integrates the different backends with minimal effort while maintaining competitive performance, reaffirming it as a robust, extensible platform that advances the design paradigm for neural simulators by achieving high generality, performance, and usability.

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将模型描述从执行中解耦:使用EDEN进行可扩展神经仿真的模块化范例。
计算神经科学模拟器传统上受到紧密耦合的仿真引擎和建模语言的限制,限制了它们的灵活性和可扩展性。改造这些平台以适应新的后端通常是昂贵的,并且在模拟器之间共享模型仍然很麻烦。本文提出了一种基于EDEN神经模拟器的替代方法,该方法引入了模块化堆栈,将抽象模型描述与执行解耦。该体系结构通过支持多个后端(包括硬件加速器)的无缝集成而无需大量重新编程,从而增强了灵活性和可扩展性。通过使用NeuroML,仿真开发人员可以专注于高性能执行,而模型用户无需实现自定义仿真引擎即可从改进的可移植性中受益。此外,将任意仿真平台(从模型优化的代码内核到定制的硬件设备)合并为后端的方法为计算神经科学社区提供了一个更具可持续性和适应性的框架。EDEN方法的有效性通过集成两个不同的后端来证明:flexHH(用于扩展霍奇金-赫胥黎网络的基于fpga的加速器)和SpiNNaker(用于大规模尖峰神经网络的知名神经形态平台)。实验结果表明,EDEN以最小的努力集成了不同的后端,同时保持了竞争力的性能,重申了它作为一个强大的、可扩展的平台,通过实现高通用性、性能和可用性来推进神经模拟器的设计范式。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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