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.
