Towards reproducible descriptions of neuronal network models.
Towards reproducible descriptions of neuronal network models.
复制标题
DOI：
10.1371/journal.pcbi.1000456
复制
发表时间：
2009-08
影响因子：
4.3
通讯作者：
Plesser HE
中科院分区：
文献类型：
--
作者：
Nordlie E;Gewaltig MO;Plesser HE
科学的进步有赖于科学家之间有效的思想交流。新的想法只有在表述准确的情况下才能得到有意义的评估和批评。这不仅适用于模拟研究，也适用于实验和理论。但经过50多年的神经网络模拟，我们仍然缺乏对计算模型在神经科学中的作用的明确和共同的理解，以及在出版物中描述网络模型的既定做法。这阻碍了对网络模型的批判性评估以及它们的重复使用。我们在这里分析了14篇提出不同复杂性的神经网络模型的研究论文，并发现在描述方法和材料的排序和放置方面，模型描述的方法差别很大。我们还观察到网络的图形表示和方程中使用的符号有很大的变化。根据我们的观察，我们提出了一个良好的模型描述实践，由出版物组织准则、模型描述核对表、模型结构表格模板和网络图准则组成。这种良好做法的主要目的是引发一场关于以人类可理解的方式传递神经网络模型的辩论，而不是机器可读的模型描述语言。我们相信，这里提出的良好的模型描述实践，以及最近在数据、模型和软件共享方面的一些其他倡议，可能会在未来几年导致计算神经科学家之间更深入和更富有成效的思想交流。我们还希望，描述和思考复杂神经网络的标准化方法的工作将使科学界对网络动力学中的高级概念有更清晰的理解，从而对大脑的功能有更深入的了解。科学家们对他们对自然的观察和模型做出了精确的、可验证的声明。然后，其他科学家可以对这些陈述进行评估，并尝试复制或扩展它们。不能复制的结果将受到适当的批评，以更好地解释实验结果或更好的模型。随着时间的推移，这篇文章发展了我们共同的科学知识。这一过程的一个关键条件是，科学家能够以一种他人可以理解和精确的方式描述他们自己的模型。本文分析了科学文献中对神经网络模型的描述，得出结论：描述网络模型的方式多种多样，这使得成功地传递模型变得困难。我们提出了一种良好的模型描述实践，以提高神经元网络模型的通信性。
Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. Scientists make precise, testable statements about their observations and models of nature. Other scientists can then evaluate these statements and attempt to reproduce or extend them. Results that cannot be reproduced will be duly criticized to arrive at better interpretations of experimental results or better models. Over time, this discourse develops our joint scientific knowledge. A crucial condition for this process is that scientists can describe their own models in a manner that is precise and comprehensible to others. We analyze in this paper how well models of neuronal networks are described in the scientific literature and conclude that the wide variety of manners in which network models are described makes it difficult to communicate models successfully. We propose a good model description practice to improve the communication of neuronal network models.
登录
查看更多内容
DOI：
10.1098/rsta.2008.0310
发表时间：
2009-05-28
影响因子：
5
作者：
Beard, Daniel A.;Britten, Randall;Yu, Tommy
通讯作者：
Yu, Tommy
影响因子：
3
作者：
Cannon, Robert C.;Gewaltig, Marc-Oliver;De Schutter, Erik
通讯作者：
De Schutter, Erik
影响因子：
2.5
作者：
Destexhe, A;Bal, T;Sejnowski, TJ
通讯作者：
Sejnowski, TJ
影响因子：
3
作者：
Gardner, Daniel;Akil, Huda;Ascoli, Giorgio A.;Bowden, Douglas M.;Bug, William;Donohue, Duncan E.;Goldberg, David H.;Grafstein, Bernice;Grethe, Jeffrey S.;Gupta, Amarnath;Halavi, Maryam;Kennedy, David N.;Marenco, Luis;Martone, Maryann E.;Miller, Perry L.;Mueller, Hans-Michael;Robert, Adrian;Shepherd, Gordon M.;Sternberg, Paul W.;Van Essen, David C.;Williams, Robert W.
通讯作者：
Williams, Robert W.
影响因子：
2.1
作者：
Donoho, David L.;Maleki, Arian;Stodden, Victoria
通讯作者：
Stodden, Victoria