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Towards reproducible descriptions of neuronal network models.

Nordlie E, Gewaltig MO, Plesser HE - PLoS Comput. Biol. (2009)

Bottom Line: Progress in science depends on the effective exchange of ideas among scientists.This hinders the critical evaluation of network models as well as their re-use.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Aas, Norway.

ABSTRACT
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.

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Diagram styles for network models.Diagrams of a model of the thalamocortical pathway drawn using diagram styles from (A) Hayot and Tranchina [6], Fig. 2, (B) Haeusler and Maass [5], Fig. 1, and (C) Lumer et al. [10], Fig. 1. Numbers on arrows in B mark connection weight and probability of connection, while line width represents the product of the two. In C, open circles show excitatory, filled circles inhibitory neurons. The model depicted is loosely based on Einevoll and Plesser [63, Fig. 3], but the differentiation into two cortical layers, each with excitatory and inhibitory subpopulations, in B and C, as well as the connection weights and probabilities, have been added here for the purpose of illustration.
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pcbi-1000456-g004: Diagram styles for network models.Diagrams of a model of the thalamocortical pathway drawn using diagram styles from (A) Hayot and Tranchina [6], Fig. 2, (B) Haeusler and Maass [5], Fig. 1, and (C) Lumer et al. [10], Fig. 1. Numbers on arrows in B mark connection weight and probability of connection, while line width represents the product of the two. In C, open circles show excitatory, filled circles inhibitory neurons. The model depicted is loosely based on Einevoll and Plesser [63, Fig. 3], but the differentiation into two cortical layers, each with excitatory and inhibitory subpopulations, in B and C, as well as the connection weights and probabilities, have been added here for the purpose of illustration.

Mentions: Paper authors draw network diagrams in quite different ways, both in the overall style of their diagrams and in use of symbols. Figure 4 shows network diagrams of a model loosely based on Einevoll and Plesser [63], Fig. 3, drawn in the style of three of the papers surveyed here. The diagram in the style of Hayot and Tranchina [6] (Fig. 4A) gives a reduced but clear overview of the overall architecture of the model; it provides no details. The style of Haeusler and Maass [5] (Fig. 4B) carries most information, with weights and probabilities shown next to connection lines, and line widths proportional to the product of weight and probability. Figure 4C, which imitates the style of Lumer et al. [10], is rather illustrative: it provides no quantitative information and the structure of the connectivity is less prominent than in the other two figures; on the other hand, it is the only figure hinting at the spatial structure of the network. Interestingly, all three diagram styles use different ways of marking excitatory and inhibitory connections: bars vs circles, black vs red, and arrows vs bars. Indeed, bars at the end of connection lines mark excitatory connections in Hayot and Tranchina's style, but inhibitory connections in the style of Lumer et al, nicely illustrating the lack of standards in the field.


Towards reproducible descriptions of neuronal network models.

Nordlie E, Gewaltig MO, Plesser HE - PLoS Comput. Biol. (2009)

Diagram styles for network models.Diagrams of a model of the thalamocortical pathway drawn using diagram styles from (A) Hayot and Tranchina [6], Fig. 2, (B) Haeusler and Maass [5], Fig. 1, and (C) Lumer et al. [10], Fig. 1. Numbers on arrows in B mark connection weight and probability of connection, while line width represents the product of the two. In C, open circles show excitatory, filled circles inhibitory neurons. The model depicted is loosely based on Einevoll and Plesser [63, Fig. 3], but the differentiation into two cortical layers, each with excitatory and inhibitory subpopulations, in B and C, as well as the connection weights and probabilities, have been added here for the purpose of illustration.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2713426&req=5

pcbi-1000456-g004: Diagram styles for network models.Diagrams of a model of the thalamocortical pathway drawn using diagram styles from (A) Hayot and Tranchina [6], Fig. 2, (B) Haeusler and Maass [5], Fig. 1, and (C) Lumer et al. [10], Fig. 1. Numbers on arrows in B mark connection weight and probability of connection, while line width represents the product of the two. In C, open circles show excitatory, filled circles inhibitory neurons. The model depicted is loosely based on Einevoll and Plesser [63, Fig. 3], but the differentiation into two cortical layers, each with excitatory and inhibitory subpopulations, in B and C, as well as the connection weights and probabilities, have been added here for the purpose of illustration.
Mentions: Paper authors draw network diagrams in quite different ways, both in the overall style of their diagrams and in use of symbols. Figure 4 shows network diagrams of a model loosely based on Einevoll and Plesser [63], Fig. 3, drawn in the style of three of the papers surveyed here. The diagram in the style of Hayot and Tranchina [6] (Fig. 4A) gives a reduced but clear overview of the overall architecture of the model; it provides no details. The style of Haeusler and Maass [5] (Fig. 4B) carries most information, with weights and probabilities shown next to connection lines, and line widths proportional to the product of weight and probability. Figure 4C, which imitates the style of Lumer et al. [10], is rather illustrative: it provides no quantitative information and the structure of the connectivity is less prominent than in the other two figures; on the other hand, it is the only figure hinting at the spatial structure of the network. Interestingly, all three diagram styles use different ways of marking excitatory and inhibitory connections: bars vs circles, black vs red, and arrows vs bars. Indeed, bars at the end of connection lines mark excitatory connections in Hayot and Tranchina's style, but inhibitory connections in the style of Lumer et al, nicely illustrating the lack of standards in the field.

Bottom Line: Progress in science depends on the effective exchange of ideas among scientists.This hinders the critical evaluation of network models as well as their re-use.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Aas, Norway.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus