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  DeNSE: modeling neuronal morphology and network structure in silico

Fardet, T., Quaresima, A., & Bottani, S. (2019). DeNSE: modeling neuronal morphology and network structure in silico. Poster presented at 28th Annual Computational Neuroscience Meeting (CNS*2019), Barcelona, Spain. doi:10.1186/s12868-019-0538-0.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-3F3A-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-1D11-8
Genre: Poster

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Fardet, T1, 2, Author              
Quaresima, A, Author
Bottani, S, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Neural systems develop and self-organize into complex networks which can generate stimulus-specific responses. Neurons grow into various morphologies, which influences their activity and the structure of the resulting network. Different network topologies can then display very different behaviors, which suggests that neuronal structure and network connectivity strongly influence the set of functions that can be sustained by a set of neurons. To investigate this, I developed a new simulation platform, DeNSE, aimed at studying the morphogenesis of neurons and networks, and enabling to test how interactions between neurons and their surroundings can shape the emergence of specific properties. The goal of this new simulator is to serve as a general framework to study the dynamics of neuronal morphogenesis, providing predictive tools to investigate how neuronal structures emerge in complex spatial environments. The software generalizes models present in previous simulators [1, 2], gives access to new mechanisms, and accounts for spatial constraints and neuron-neuron interactions. It has been primarily applied on two main lines of research: a) neuronal cultures or devices, their structures being still poorly defined and strongly influenced by interactions or spatial constraints [3], b) morphological determinants of neuronal disorders, analyzing how changes at the cellular scale affect the properties of the whole network [4]. I illustrate how DeNSE enables to investigate neuronal morphology at different scales, from single cell to network level, notably through cellcell and cell-surroundings interactions (Fig. 1). At the cellular level, I show how branching mechanisms affect neuronal morphology, introducing new models to account for interstitial branching and the influence of the environment. At intermediate levels, I show how DeNSE can reproduce interactions between neurites and how these contribute to the final morphology and introduce correlations in the network structure. At the network level, I stress how networks obtained through a growth process differ from both simple generative models and more complex network models where the connectivity comes from overlaps of real cell morphologies. Eventually, I demonstrate how DeNSE can provide biologically relevant structures to study spatio-temporal activity patterns in neuronal cultures and devices. In these structures, where the morphologies of the neurons and the network are not well defined but have been shown to play a significant role, DeNSE successfully reproduces experimental setups, predicts the influence of spatial constraints, and enables to predict their electrical activities. Such a tool can therefore be extremely useful to test structures and hypotheses prior to actual experiments, thus saving time and resources.

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 Dates: 2019-072019-11
 Publication Status: Published in print
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 Identifiers: DOI: 10.1186/s12868-019-0538-0
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Title: 28th Annual Computational Neuroscience Meeting (CNS*2019)
Place of Event: Barcelona, Spain
Start-/End Date: 2019-07-13 - 2019-07-17

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Title: BMC Neuroscience
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: - Volume / Issue: 20 (Supplement 1) Sequence Number: P21 Start / End Page: 22 Identifier: ISSN: 1471-2202
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905018