Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans

Abstract

We investigate synchronization patterns and chimera-like states in the modular multilayer topology of the connectome of Caenorhabditis elegans. In the special case of a designed network with two layers, one with electrical intra-community links and one with chemical inter-community links, chimera-like states are known to exist. Aiming at a more biological approach based on the actual connectivity data, we consider a network consisting of two synaptic (electrical and chemical) and one extrasynaptic (wireless) layers. Analyzing the structure and properties of this layered network using Multilayer-Louvain community detection, we identify modules whose nodes are more strongly coupled with each other than with the rest of the network. Based on this topology, we study the dynamics of coupled Hindmarsh-Rose neurons. Emerging synchronization patterns are quantified using the pairwise Euclidean distances between the values of all oscillators, locally within each community and globally across the network. We find a tendency of the wireless coupling to moderate the average coherence of the system: for stronger wireless coupling, the levels of synchronization decrease both locally and globally, and chimera-like states are not favored. By introducing an alternative method to define meaningful communities based on the dynamical correlations of the nodes, we obtain a structure that is dominated by two large communities. This promotes the emergence of chimera-like states and allows to relate the dynamics of the corresponding neurons to biological neuronal functions such as motor activities.

Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

“…As there are significant mechanistic and functional differences (e.g. speed, modulatory role) between transmission modes, the neuronal connectome can be comprehended as a multiplex network of partially independent layers with each layer representing a distinct type of signaling [ 47 , 72 , 73 ]. Two dedicated cases were monoamine and metabotropic transmission which were excluded from our workflow.…”

“…Wireless networks possibly exist for ionotropic receptors as well via mechanism of spillover transmission [10,76]. Likewise, metabotropic neurotransmission typically acting via G-protein coupled receptors plays a rather modulatory than direct excitatory/inhibitory role in the nervous system by inducing broad, longlasting, slow time-scale changes which is distinctive [71,73,[77][78][79][80]. Our paper covers ligandgated ionotropic synaptic connections, which account for the fast-acting system of neurotransmitter-mediated synaptic signaling.…”

Remote pacemaker control of chimera states in multilayer networks of neurons

“…During the last two decades chimeras have become a popular topic in the nonlinear science community [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] and captured the interest of scientists from various disciplines, ranging from neuroscience, chemistry, to engineering and many others. This widespread interest in the phenomenon led to the establishment of conceptual links between chimera states and real-world dynamics [7,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] and also to the design of experiments which led to observation of chimera states in the laboratory [36][37][38][39][40][41].…”

Machine Learning Assisted Chimera and Solitary States in Networks

“…In such scenarios, the multiplex (multilayer) framework turns out to be an apt contender in representing different dynamical processes acting on the same set of units through different layers comprising different genres of links having different connectivity among the same set of interlinked nodes [33][34][35]. Recently, the investigations pertaining to the emergence of chimera states and solitary states have been extended to multilayer networks subjected to a variety of dynamical models [36][37][38][39][40][41][42].…”