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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/19662159
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. 2009 Aug;5(8):e1000456.
doi: 10.1371/journal.pcbi.1000456. Epub 2009 Aug 7.

Towards reproducible descriptions of neuronal network models

Eilen Nordlie  1 , Marc-Oliver Gewaltig, Hans Ekkehard Plesser
Affiliations

Towards reproducible descriptions of neuronal network models

Eilen Nordlie et al. PLoS Comput Biol. 2009 Aug.

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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Conflict of interest statement

EN and HEP are paid employees of the Norwegian University of Life Sciences. M-OG is a paid employee of Honda Research Institute Europe GmbH.

Figures

Figure 1
Figure 1. Placement of description in papers surveyed.
Bar graphs show the percentage of papers describing (from top to bottom) model architecture, model connectivity and neuronal dynamics in the body text of the paper, the appendix, and in supplementary material. Many papers spread descriptions over several locations and are thus counted in several categories. For detailed data, see supporting material Tables S1, S2 and S3.
Figure 2
Figure 2. Use of different means of description in papers surveyed.
Bar graphs show the percentage of papers describing (from top to bottom) model architecture, model connectivity and neuronal dynamics using prose, equations, figures, tables, and references. Many papers combine several means for one purpose and are thus counted in several categories. For detailed data, see supporting material Tables S1, S2, S3.
Figure 3
Figure 3. Interpretation of Lumer model architecture.
The most parsimonious interpretation of the description of the primary visual cortical area Vp given by Lumer et al, is as two layers of 40×40 topographic elements, representing horizontal and vertical orientations, respectively.
Figure 4
Figure 4. Diagram styles for network models.
Diagrams of a model of the thalamocortical pathway drawn using diagram styles from (A) Hayot and Tranchina , Fig. 2, (B) Haeusler and Maass , Fig. 1, and (C) Lumer et al. , 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.
Figure 5
Figure 5. Tabular description of Brunel model .
The model is summarized in panel A and detailed in panels B–F.
Figure 6
Figure 6. Alternatives for diagrams of simple network models (Brunel [10]).
(A) Excitatory connections shown by full lines, inhibitory by dashed lines. Lines beginning with open semicircle and ending in filled circle indicate random convergent connections. (B) Double lines represent multiple connections, solid/dashed marks excitatory/inhibitory connections. Multiplicity of connections marked at line ends. (C) Same as B, but inhibitory connections marked with circles on target side instead of dashed lines. (D) Same as C, but displaying explicitly that there are formula image external Poisson inputs (PG) to each neuron, and single lines are used instead of double lines.
Figure 7
Figure 7. Tabular description of Lumer et al. model , part 1.
The model is summarized in panel A and detailed in panels B–I. See Figure 8 for panels E–I.
Figure 8
Figure 8. Tabular description Lumer et al. model , part 2.
See Figure 7 for panels A–D.
Figure 9
Figure 9. Hierarchy of diagrams of a complex network model (Lumer et al. [10]).
(A) Overview diagram of connectivity between high-level populations. Excitatory connections are marked by arrows, inhibitory connections by circles. Excitatory and inhibitory populations have been lumped in Tp, while Vp(v) and Vp(h) are composed of three layers of excitatory and inhibitory populations, as detailed in B. (B) Detailed diagram of connectivity within cortical population Vp(v), which is tuned to vertically oriented stimuli. Vp(v) is composed of three cortical layers, each with an excitatory (left) and inhibitory (right) subpopulation. Filled arrows mark excitatory, open circles inhibitory connections. Connections to and from corresponding horizontally tuned cortical populations in Vp(h) are shown as dashed lines; black lines show input from the thalamus. Connections to and from higher cortical areas are not shown. (C) Detailed rendition of connection masks and kernels projecting onto one cortical subpopulation Vp(v)LI(e) from panel B, i.e., the excitatory subpopulation of the infragranular layer of Vp(v). Squares show projection masks, gray shade the probability of a connection (black: formula image). Connections are created by centering the mask about each location in the layer and drawing connections according to the probability distribution. Outgoing arrows indicate projections to other populations. Projection masks are scaled down in size to fit all projections into the layer, and grayscales have been adjusted for visibility. Connections are placed to correspond to the layout of panel B: Connections to and from thalamus are at the bottom, connections to and from Vp(v)LI(i) and Vp(h) to the right and connections to and from Vp(v)LS and Vp(v)L4 at the top.
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