Computing Temporal Sequences Associated With Dynamic Patterns on the C. elegans Connectome.

Vivek Kurien George,Francesca Puppo,Gabriel A Silva

doi:10.3389/fnsys.2021.564124

Vivek Kurien George, Francesca Puppo + Show 1 more

Open Access

https://doi.org/10.3389/fnsys.2021.564124

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Abstract

Understanding how the structural connectivity and spatial geometry of a network constrains the dynamics it is able to support is an active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motorneuron activations in ventral (VB) and dorsal (DB) classes of motorneurons emerged from the simulations, which are qualitatively similar to rhythmic motorneuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent—and we propose—purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.

Highlights

A variety of network analyses methods are widely used to study complex systems (Newman, 2010; Varshney et al, 2011; Deo, 1975; Bassett et al, 2017; Boccaletti et al, 2014, 2006; Sporns et al, 2005)
As a first attempt to study the affects of a geometric embedding on the dynamics supported by the C. elegans connectome, we intentionally focused on the simplest node model applicable within the used signaling framework (Silva, 2019)
Our results indicate that there is value in our approach, laboratory experiments have established that many C. elegans neurons may not communicate using discrete signaling dynamics, but rather through graded isopotential dynamics

Summary

Introduction

A variety of network analyses methods are widely used to study complex systems (Newman, 2010; Varshney et al, 2011; Deo, 1975; Bassett et al, 2017; Boccaletti et al, 2014, 2006; Sporns et al, 2005). Understanding how the structural connectivity of a network constrains the dynamics it is able to support is still an active and open area of research This is the case for spatial-temporal networks (Holme, 2015), where much of the literature and methods are still in early stages and mostly descriptive, providing few tools for predictive modeling. We introduce a novel set of methods and analyses for this task, and apply it to study the dynamics of the Caenorhabditis elegans worm connectome as an example. This remains at the forefront of neuroscience research (Bargmann and Marder, 2013; Buonomano and Maass, 2009)

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