Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics

Frontiers in Computational Neuroscience

J. Nathan Kutz¹  Andrew Walker¹  Eli Shlizerman²  James M. Kunert-Graf³ 

[1] Department of Applied Mathematics, University of WashingtonSeattle, WA, United States;Department of Electrical Engineering, University of WashingtonSeattle, WA, United States;Department of Physics, University of WashingtonSeattle, WA, United States;

关键词: multistability;    transient dynamics;    nonlinear networks;    C. elegans;    bifurcations;   

DOI  :  10.3389/fncom.2017.00053

来源: DOAJ

【 摘 要 】

The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs. Specifically, we generate bifurcation diagrams for all possible single-neuron inputs, showing the existence of fixed points and limit cycles for different input regimes. The nature of the dynamical response is seen to vary according to the type of neuron receiving input; for example, input into sensory neurons is more likely to drive a bifurcation in the system than input into motor neurons. As a specific example we consider compound input into the neuron pairs PLM and ASK, discovering bistability of a limit cycle and a fixed point. The transient timescales in approaching each of these states are much longer than any intrinsic timescales of the system. This suggests consistency of our model with the characterization of dynamics in neural systems as long-timescale transitions between discrete, low-dimensional attractors corresponding to behavioral states.

【 授权许可】

Unknown