Chimera-like States in Modular Neural Networks.

Area of Science:

• Neuroscience
• Complex Systems
• Computational Biology

Background:

• Chimera states, characterized by coexisting coherent and incoherent dynamics, have been studied in complex networks.
• Their occurrence in modular networks, however, remains less explored.

Purpose of the Study:

• To investigate the emergence of chimera states in a modular neural network inspired by the C. elegans connectome.
• To analyze the role of electrical and chemical synapses in shaping network dynamics.

Main Methods:

• Simulated a modular neural network with six interconnected communities, each exhibiting chaotic bursting dynamics.
• Employed electrical synapses for within-community connections and chemical synapses for cross-community connections.
• Utilized the Kuramoto order parameter (ρ), chimera-like (χ), and metastability (λ) indices to quantify synchronization.

Main Results:

• Demonstrated that the interplay of electrical and chemical synapses can induce chimera-like states in the modular network.
• Observed that synchronized neurons tend to belong to larger communities, while desynchronized neurons are found in smaller ones.
• Confirmed similar findings in networks with features analogous to the C. elegans neural network.

Conclusions:

• Chimera-like states are prominent phenomena in modular networks under specific conditions.
• The findings offer insights into the behavior of complex modular systems, including biological neural networks.