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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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Related in: MedlinePlus

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: ). 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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pcbi-1000456-g009: 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: ). 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.

Mentions: We will discuss these elements in turn below, followed by more detailed discussions about how to render specific aspects of a network model. As an illustrative example, Figures 5 and 6 provide a concise description of the Brunel [3] model following the good practice format. A similar description of the Lumer et al. [10] model is given in Figures 7–9.


Towards reproducible descriptions of neuronal network models.

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

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: ). 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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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2713426&req=5

pcbi-1000456-g009: 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: ). 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.
Mentions: We will discuss these elements in turn below, followed by more detailed discussions about how to render specific aspects of a network model. As an illustrative example, Figures 5 and 6 provide a concise description of the Brunel [3] model following the good practice format. A similar description of the Lumer et al. [10] model is given in Figures 7–9.

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