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

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

Front Syst Neurosci. 2021 Mar 9:15:564124. doi: 10.3389/fnsys.2021.564124. eCollection 2021.

Authors

Vivek Kurien George^{1

2}, Francesca Puppo^{1

2

3}, Gabriel A Silva^{1

2

3

4}

Affiliations

¹ Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.
² Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States.
³ BioCircuits Institute, University of California, San Diego, San Diego, CA, United States.
⁴ Department of Neurosciences, University of California, San Diego, San Diego, CA, United States.

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.

Keywords: C. elegans model; computational modeling; connectome analysis; graph theory; networks (circuits).