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Emergent Dynamics and Spatio Temporal Patterns on Multiplex Neuronal Networks.

Umesh Kumar Verma¹, G Ambika¹

¹Department of Physics, Indian Institute of Science Education and Research Tirupati, Tirupati, India.

Frontiers in Computational Neuroscience

|December 20, 2021

Summary

This summary is machine-generated.

This study explores how neurons in a multiplex network create spatio-temporal patterns. Different coupling types, like excitatory and inhibitory, lead to synchronized oscillations and pattern selection between layers.

Keywords:

mixed-mode oscillations multi-cluster synchronization multiplex network neuronal network synchronization

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Area of Science:

Computational Neuroscience
Complex Systems
Network Science

Background:

Neurons exhibit complex spatio-temporal dynamics.
Multiplex networks offer a framework to study interconnected systems.
Understanding emergent behavior in coupled neuronal systems is crucial.

Purpose of the Study:

To investigate spatio-temporal pattern emergence in a two-layer multiplex neuronal network.
To analyze the effects of different intralayer and interlayer coupling types (excitatory, inhibitory, electrical).
To explore control mechanisms for pattern selection via coupling strengths.

Main Methods:

Utilized the Hindmarsh-Rose model for single neuron dynamics.
Simulated neuronal activity in a two-layer multiplex network.
Analyzed emergent spatio-temporal patterns under various coupling conditions.

Main Results:

Excitatory coupling led to in-phase synchronized oscillations and amplitude death.
Inhibitory coupling resulted in anti-phase mixed-mode oscillations (MMO) with phase regularities.
Multiplexing revealed pattern transfer and selection between layers, with oscillation revival and phase transitions.
Electrical and synaptic coupling combinations produced synchronized in-phase or anti-phase activity.

Conclusions:

Coupling type and strength significantly influence spatio-temporal patterns in multiplex neuronal networks.
Pattern selection and transfer between layers are controllable phenomena.
The study provides insights into the collective behavior of complex neuronal systems.